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Article

Understanding the Dynamics of Source-Apportioned Black Carbon in an Urban Background Environment

by
Daria Pashneva
,
Agnė Minderytė
,
Lina Davulienė
,
Vadimas Dudoitis
and
Steigvilė Byčenkienė
*
SRI Center for Physical Sciences and Technology, Department of Environmental Research, Saulėtekio Ave. 3, 10257 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(7), 832; https://doi.org/10.3390/atmos15070832
Submission received: 30 April 2024 / Revised: 24 June 2024 / Accepted: 5 July 2024 / Published: 11 July 2024
(This article belongs to the Special Issue Aerosols Pollution: Characteristics, Impacts, Projections and Mitigation)
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Versions Notes

Abstract

:
This study aims to delineate the characteristics of black carbon (BC) in the atmosphere over the urban background environment in Vilnius (Lithuania) from 1 June 2021 to 31 May 2022 using aethalometer (Magee Scientific) measurements. The annual mean concentrations of BC originating from fossil fuels (BCff) and from biomass burning (BCbb) were found to be 0.63 μg m−3 with a standard deviation (SD) of 0.67 μg m−3 and 0.27 µg m−3 (0.35 μg m−3). The further findings highlight the dominance of fossil-fuel-related BC throughout the study period (71%) and the seasonal variability of BC pollution, with biomass-burning-related BC making the largest contribution during the summer season (41%) and the smallest contribution during autumn (23%). This information provides valuable insights into the sources and dynamics of BC pollution in the region. The sources and composition of BC on the days with the highest pollution levels were influenced by a combination of local and regional factors in every season. Additionally, this study employs an advanced approach to understanding urban BC pollution by focusing on high-pollution days (18), identified based on a daily mean BC mass concentration exceeding the 95th percentile, alongside an analysis of overall seasonal and diurnal variations. This methodology surpasses many those of previous urban BC studies, offering a comprehensive examination of the sources and composition of BC pollution.

1. Introduction

Since its initial mention in the 1970s by Hansen et al. (1984) and Rosen and Novakov (1983) [1,2] black carbon (BC) has remained a focal point in environmental research, driven by the persistent challenge of particulate matter (PM) pollution amidst global urbanization [3]. BC is a component of aerosol particles with a short lifetime in the atmosphere (from a few days to two weeks) [4] resulting from incomplete combustion processes and has an impact on regional and global air quality, public health, climate change and ecosystems; it is a more accurate indicator of harmful particulate matter from combustion sources, particularly road traffic, than undifferentiated PM mass [5,6]. Due to its small size (less than 2.5 μm), it can be easily inhaled and affect human health by causing cardiovascular, respiratory and other diseases [6,7,8,9].
There are large uncertainties in the estimation of BC emissions globally due to limited information on the relative contributions from fossil fuel combustion and biomass burning to total BC emissions. The contributions to BC emissions from fossil fuels and biofuels are estimated to be 40% and 20%, respectively. In urban areas worldwide, anthropogenic sources predominantly contribute to BC emissions, with transportation and residential combustion accounting for approximately 60–62% and 24–28% of total BC emissions, respectively [10,11]. These relative source contributions vary significantly due to the spatio-temporal variability in BC emissions, posing persistent challenges in regulating these emissions [12,13,14,15].
Source apportionment is at the cutting edge of environmental science, employing a variety of advanced techniques and technologies to accurately determine the sources of pollutants such as BC, including stable isotope analysis [16], high-resolution mass spectrometry [17,18], positive matrix factorization [19,20], machine learning [21] and remote sensing [22,23]. Sophisticated atmospheric models are employed to simulate the transport, transformation and deposition of BC, facilitating the linkage of observed mass concentrations with their respective sources. Given the complex nature of BC pollution, the need for precise source apportionment between fossil fuel combustion and biomass burning is critical to effectively address urban air quality issues. Furthermore, BC mass concentrations not only vary due to emission sources but also exhibit significant seasonal, weekly and diurnal variations. Addressing these complexities through advanced source apportionment techniques is crucial for developing targeted and effective air quality management strategies and model accuracy.
From a source apportionment perspective, the daily fluctuations in BC concentration levels are commonly linked to variations in human activities. During weekdays, the increased intensity of residential heating and the higher traffic volume, particularly during morning and evening rush hours, contribute to elevated BC levels. In contrast, the differences in BC levels between weekdays and weekends can be attributed to the changes in the intensity of these anthropogenic activities. On weekends, when residential heating demands and traffic volumes are typically lower, the BC concentrations tend to be lower than on weekdays [24,25]. Seasonally, the highest BC levels occur in winter, followed by spring and autumn, and the lowest in summer, influenced by both anthropogenic activities and meteorological factors [26,27]. Several studies have revealed that local meteorological factors play a significant role in determining air pollutant concentrations [28,29,30]. Additionally, the co-existence of BC with NOx and PMs in urban areas suggests a potential for synergistic interactions between these two pollutants. The shared sources of BC and NOx, primarily from combustion-related activities in urban environments, contribute to their co-occurrence. This indicates that the dynamics between BC and NOx are an important consideration when studying source apportionment, as emissions from transport can serve as valuable tracers [31,32,33,34]. Integrating the dynamics of these pollutants with emission data and atmospheric modelling can provide comprehensive insights into the complex interactions between anthropogenic activities and atmospheric processes [35,36,37]. While legal regulation currently exists solely in relation to PM10, it is already recognised that reducing BC concentrations is a more effective approach from a public health perspective [38].
While there has been extensive global research on BC, including its sources, impacts and interactions with other pollutants, a significant gap remains in understanding these dynamics and sources in specific regional contexts, such as Baltic countries. This study addresses these gaps by focusing on an urban background environment, where comprehensive measurements of BC and concurrent meteorological parameters are conducted across four seasons.

2. Materials and Methods

2.1. Site Description

The BC mass concentration sampling campaign was conducted at the SRI Center for Physical Sciences and Technology (FTMC), 54.72° N and 25.32° E, in the capital and largest city of Lithuania (Vilnius) throughout the year from 1 June 2021 to 31 May 2022. The city is located in Northern Europe and has a total area of 402 km2 (Figure 1). The Vilnius administrative zone has around 581 thousand inhabitants (Statistics Lithuania, 2023, https://osp.stat.gov.lt/gyventojai1 (accessed on 6 July 2024)).
The sampling site is located about 8 km northeast of the city centre, 3 km from industrial facilities and about 600 m from the high-intensity urban road with a forested area in between. The surroundings of this station are representative of the urban background environment of Vilnius overall.

2.2. Black Carbon Mass Concentration and Source Apportionment

An aethalometer (Magee Scientific, model AE-31, Ljubljana, Slovenia (June–August 2021) or AE-33, Berkeley, CA, USA (September–May 2022)), was used for continuous real-time measurements of BC mass concentration with a temporal resolution of 1 min at a flow rate of 4.9 L min−1. A consistency check was performed upon changing the aethalometer model to ensure data integrity. Furthermore, hourly averages of BC mass concentrations were used for analysis. The optical transmission of carbonaceous aerosol particles was measured sequentially at seven wavelengths (λ = 370, 470, 520, 590, 660, 880 and 950 nm). The standard channel for BC measurements is 880 nm, as it is considered to be the predominant absorber. The source apportionment analysis was performed using specific aerosol absorption Ångström exponent values for biomass burning (AAEbb, 2.2) and fossil fuels (AAEff, 0.9), which were determined for an urban background in Vilnius in papers by Drinovec et al., 2015; Minderytė et al., 2022; and Sandradewi et al., 2008 [39,40,41]. The detection range of the aethalometer is from 0.1 to 100 μg m−3 according to the manual of the instrument [42]. The time zone used in this study is UTC + 2:00. In this study, four seasons are defined as spring (March, April and May 2022), summer (June, July and August 2021), autumn (September, October and November 2021) and winter (December 2021, January and February 2022).
The hourly averages of temperature (T, °C), relative humidity (RH, %), barometric pressure (Pr, hPa), wind speed (WS, m s−1) and wind direction (WD, degrees), as well as the mass concentrations of air pollutants (NOx and PM10), were provided by the Environmental Protection Agency of Lithuania (https://www.gamta.lt). The concentration of PM10 was determined by the APDA-371 instrument through the measurement of beta ray attenuation. The APNA-370 instrument was utilized for the measurement of NOx, utilizing a cross-flow modulated semi-decompression chemiluminescence methodology.

2.3. HYSPLIT Model and MODIS Satellite Data

An analysis of the 72-h backward trajectories of air masses arriving in Vilnius at 500 m, 1000 m and 1500 m above the ground was performed using the HYSPLIT4 (Hybrid Single-Particle Lagrangian Integrated Trajectory) model of the Air Resources Laboratory (ARL) with the aim of investigating the contribution of air masses’ long-range transport to BC mass concentrations.
The fire data obtained originate from the Resource Management System (FIRMS) operated by the NASA/GSFC Earth Science Data Information System (ESDIS) (https://firms.modaps.eosdis.nasa.gov/map (accessed on 5 June 2024)). The data are derived from satellite observations that detect thermal anomalies associated with open fires reaching temperatures greater than 2000 K. The MODIS and the Navy Aerosol Analysis and Prediction System (NAAPS) global aerosol model data were used to profile fire location maps over Lithuania. The presence of a smoke layer over Vilnius was confirmed by a combination of NAAPS model data and BC observations.

2.4. Conditional Probability Field (CPF) Analysis

The BC mass concentration, WS and WD data were used to generate bivariate polar plots using the R statistical and Openair software packages, version 4.3.1. The conditional probability function (CPF) method is effective in identifying major sources by calculating the probability of a species concentration exceeding a certain value (in this paper, above the 75th percentile) in a particular wind sector. The method is described in detail by Ashbaugh et al., 1985 [43].

3. Results and Discussion

3.1. Temporal Variation of BC and Source Apportionment

This study on BC analysis provides a comprehensive analysis approach that spans from time series analysis to seasonal, daily and high-pollution-day assessments. The time series of the BC mass concentration and the temperature as well as the box plots for each month are shown in Figure 2 and Table 1. The annual mean mass concentration of BC was 0.89 μg m−3 with a standard deviation of 0.99 μg m−3, indicating high variability of the BC level. The high variability of BC levels has been repeatedly demonstrated in various studies [24,25,44]. The highest mean mass concentration of BC (1.14 µg m−3) was observed during the winter season, which is attributable to the lower boundary layer in the winter season and to the higher demand for residential heating and biomass burning [45]. The lowest monthly mean value was obtained for July (0.50 μg m−3), which can be attributed to two factors: firstly, the high boundary layer observed throughout the year [45], and secondly, the timing of the vacation period, which resulted in reduced traffic activity within the city.
The comparison between the values of BC detected in Vilnius and those reported for other European and non-European countries is presented in Table 2. The results demonstrate a similarity between the recorded BC mass concentration in Vilnius and those observed at the National Atmospheric Observatory Kosetice (NAOK), Czech Republic, in 2013 and 2014 (0.99 and 0.84 µg m−3, respectively), and Helsinki, Finland (0.88 µg m−3, December 2015–December 2016), and comparable to many sites in Europe during different periods, for example in Amsterdam and Rotterdam, Netherlands (1.09 and 1.10 µg m−3, respectively January–July 2013), Helsinki, Finland (1.69 µg m−3, October 2015–May 2017), and Zabrze, southern Poland (3.22 µg m−3, April 2019–March 2020). This study differs from previous studies in Lithuania in that it is the first to examine the relationship between BC concentrations and meteorological conditions and other air pollutants. Prior studies have focused on long-range transport, annual and seasonal variations in the coastal region of Lithuania (Preila) and the deposition of particles on trees [32,33,34,46,47,48]. This study is the first to investigate the relationship between BC concentrations and meteorological conditions and other air pollutants, and it differs from previous studies in Lithuania. Previous studies have focused on long-range transport, annual and seasonal variations in the coastal region of Lithuania (Preila) and particle deposition on trees [35,36,37].
Table 2. Mean BC values in urban and rural background environments. Here, UB means an urban background, RB means a rural background and SUB means a suburban background.
Table 2. Mean BC values in urban and rural background environments. Here, UB means an urban background, RB means a rural background and SUB means a suburban background.
Place (Environment)Time PeriodConcentration
(μg m−3)		Reference
Vilnius, Lithuania (UB)6/2021–5/20220.89This study
Madrid, Spain (urban site with traffic)
Madrid, Spain (UB)
Madrid, Spain (RB)		2014–20153.70
2.33
2.61		[49]
National Atmospheric Observatory Kosetice (NAOK), Czech Republic (RB)20130.99[27]
20140.84
20150.64
20160.58
20170.65
Zabrze, southern Poland (UB)4/2019–3/20203.22[11]
Amsterdam, Netherlands (UB)1–7/20131.09[50]
Rotterdam, Netherlands (UB)1–7/20131.10
Jinan, China (UB)9–11/20183.6[51]
12/2018–1/20195.8
Wanzhou District,
China (UB)		6/2013–2/20184.4[26]
Sofia, Bulgaria (UB)10/20202.4[28]
1/20213.6
Helsinki, Finland (UB)
Helsinki, Finland (SUB)		10/2015–5/2017
12/2015–12/2016
1/2017–5/2017		1.69
0.88
1.04		[10]
Paris, France (UB)5/2012–12/2018
Autumn		0.34[52]
Winter0.32
Spring0.24
Summer0.25
The annual mean mass concentration of BC from fossil fuel (BCff) and biomass burning (BCbb) were found to be 0.63 μg m−3 (0.67 μg m−3) and 0.27 μg m−3 (0.35 μg m−3), respectively. The average fraction of BCbb in BCtotal across the seasons varied ranging from 23 to 41%. The largest contribution of BCbb (41.0%) was observed in summer, while the lowest was observed in autumn (23%). Winter and spring levels were comparable, accounting for 30.0 and 27.0%, respectively.
Variations in BC mass concentrations highlight the complex interactions between emission sources, meteorological conditions and human activities in urban environments. BC concentration levels tend to be higher during the winter (1.14 µg m−3) months compared to summer (0.55 µg m−3), a phenomenon that can be attributed to increased biomass burning for the purpose of heating and the lower temperatures (Table 1, Figure 3). A similar pattern was observed in Zhengzhou, China, and Madrid, Spain [49,53]. This is reflected in the highest concentrations of biomass burning BCbb being observed in winter (0.35 µg m−3) and the lowest in autumn (0.23 µg m−3). It is notable that the BCff concentration is also highest in winter (0.78 µg m−3) in comparison to other seasons. In the spring season, particularly in March, grass burning can significantly contribute to elevated levels of air pollutants, including BC, by long-range transport from neighbouring regions, especially non-European countries [54,55,56]. This is supported by the presence of active fire detections in the FIRMS MODIS database and NAAPS model results, which indicate higher concentrations of smoke during March (discussed in Section 3.3).
The day-of-week trends in BC mass concentration for each season are shown in Figure 4. The highest level of BC mass concentration was observed on Friday for Summer and Autumn reaching 0.66 and 0.88 µg m−3 respectively, on Wednesday in winter with a BC mass concentration of 1.66 µg m−3 and on Tuesday in spring with a BC concentration of 1.33 µg m−3. Conversely, the lowest level of BC mean mass concentration was observed on Sunday for the summer, autumn and spring seasons, with values of 0.41 µg m−3, 0.70 µg m−3 and 0.81 µg m−3, respectively. For the winter season, the lowest concentration was observed on Saturday, with a value of 0.77 µg m−3. The weekly BC mass concentrations showed an increasing trend across weekdays (Monday to Friday) and a strong decrease during weekends (Saturday and Sunday) due to the anthropogenic influence (significant reduction of traffic). These patterns were also observed in the other studies [29,57].
In order to reveal important insights into the temporal variability of source apportioned BC, the diurnal profile and frequency distribution of BCff and BCbb mass concentrations during different seasons were analysed. As shown in Figure 5, the daily cycles of BCff and BCbb in all four seasons showed similar patterns of variation. Morning peaks were consistently observed between 07:00 and 09:00 during the spring and summer seasons, while those in the winter and autumn seasons occurred slightly later, between 08:00 and 11:00; an evening peak was also observed in all four seasons. It is worth noting that the shift from winter to summer time affects the time of the peak occurrence.
The histogram statistics of BCbb and BCff for the four seasons were calculated in order to gain insights into the variability of BC concentration values illustrating the variability of the quantity of emissions from biomass-burning- and transport-related sources. The frequency distribution of BCff mass concentration in summer was relatively narrow (up to 0.4 µg m−3), presenting a typical single peak distribution when biomass burning activities are less prevalent, whereas during autumn and winter, the higher BCff frequencies are below 1 µg m−3.
Hourly mass concentrations of BC ranged from a few micrograms up to 12.33 µg m−3 and those of BCbb and BCff from a few micrograms up to 5.64 μg m−3 and 8.48 μg m−3, respectively.

3.2. Analysis of Meteorological Variables

In Vilnius, where the climate is classified (Kottek et al., 2006) [58] as a humid continental with four distinct meteorological seasons, understanding the influence of meteorological conditions on pollution levels is crucial. Factors such as T, WS, RH and precipitation exhibit notable variability throughout the year, which can significantly impact the dispersion, transformation and removal of pollutants in the atmosphere. Table 3 presents descriptive statistics for various meteorological variables across four seasons. The average air temperature during the one-year measurement period was 7.8 °C with a high variability of temperatures ranging from −15.6 °C to 35.5 °C, which is a lite bit higher than statistical information from previous time (7.2 °C). The climatic seasonal mean values of air temperature in Vilnius for previous years starting from summer, the seasonal mean values were 17.5, 7.1, −0.5 and 7 °C). However, a higher mean air temperature in summer by more than 2 °C and a lower one in winter means a wider range of temperature fluctuations during the analysed year.
The data show that the average temperature in summer 2022 was 19.8 °C, with a high of 35.0 °C observed in June. In addition, the summer months were characterised by the lowest average WS of 0.15 m s−1 and the lowest values for WS and air Pr compared to other seasons.
Winter was characterized by the lowest average temperature (−2.3 °C) and temperature minimum (−15.6 °C), as well as the highest average humidity (87%) and the highest average WS (1.1 m s−1) with the largest fluctuation range (Table 3).
All seasons had temperature variation ranges exceeding 20 °C. However, during autumn and spring, there is a most significant temperature range that is similar between the two seasons. Spring also showed greatest fluctuations in humidity and air pressure and had the lowest average humidity compared to other seasons.
Winds from the northwest (NW) occurred frequently in all four seasons during the period studied, with the highest frequency in spring (17%) and the lowest in autumn (10%). In winter and spring, winds from the WNW dominated with 24% and 20% respectively. In summer, the predominant WD was south, accounting for 15% of the total amount of WD observations.
The average BC mass concentration as a function of WS and direction was determined for each season using a polar coordinate system (Figure 5). In each polar diagram, the distance from the centre represents the WS (m s−1), with greater distances indicating higher WS. The colour scale on the diagram represents the average BC mass concentration. The results indicate that the highest BC levels for all seasons were observed during periods of low WS (WS < 0.5 m s−1), suggesting that high WS help disperse locally emitted air pollutants. Additionally, a WD from the northwest at WS of 3–3.5 m s−1 coincided with the relatively low BC mass concentrations during the autumn, winter and spring seasons. Low concentrations could be attributed to high WS, which promote dispersion, and the relatively cleaner air flowing from the Baltic Sea compared to the more urban areas on the mainland.
An important characteristic that is revealed when considering BC concentration intervals is that sources also tend to occupy clear concentration intervals. Therefore, in order to identify the presence of distinct sources, data filtering is required, which involves three components: WS, WD and concentration interval (>75th percentile) (Figure 6). The polar diagram demonstrates that during the winter period, the highest BC mass concentration is associated with conditions of calm WS and local and south-situated sources. In contrast, no source occurred in that direction during the summer. Furthermore, it has been observed that there is an increase in BC mass concentrations during the autumn season when the wind blows from the south-easterly, easterly and south-westerly directions. This phenomenon could be explained by the presence of households in these areas that use biomass for household heating purposes. The analysis of the CPF at the >75th percentile for autumn clearly indicates the presence of an additional source in a south-south-westerly direction at higher WS of 1.0–2.5 m s−1, during winter (in the southern region at WS between 1.0 and 1.55 m s−1) and spring (in the eastern region at speeds between 0.5 and 1.05 m s−1).
Matrices of the Pearson correlation coefficients between air pollutants, specifically BC, PM10 and NOx, and meteorological parameters, namely WS, WD, RH, T and Pr, for the summer, autumn, winter and spring months are presented in Figure 7. It can be observed that the correlation between BC and other parameters varies depending on the season. The results obtained indicate significant correlation (r = 0.64) between the mass concentrations of BC and NOX throughout the year and conversely a weak positive correlation between BC and PM10 (r = 0.36) and Pr (r = 0.25). The tightest relation was observed only with NOx during all seasons, varying from 0.58 in summer and 0.73 in winter. A correlation analysis was performed on data collected over all seasons in order to investigate the relationship between BC and meteorological conditions. The results indicated a weak to moderate correlation during all seasons, with the highest negative correlation (r = −0.42) observed during the winter period; however, it is important to note that there was also a strong positive correlation between BC and NOx (r = 0.73) and PM10 (r = 0.52). During the summer season, however, there was negligible correlation of BC with PM10. Of the four seasons, only summer exhibits a weak positive correlation with relative humidity (r = 0.25). In winter and spring, BC shows a weak negative correlation with temperature and a moderately positive correlation with Pr, particularly during spring (r = 0.45).

3.3. Analysis of the High BC Mass Concentration Events

The data presented in the previous sections clearly show that the seasonal difference can be observed not only in the meteorological conditions but also in the average BC concentrations, as well as in the ratio in the source apportionment. Consequently, each season was analysed separately, resulting in the identification of 18 high-pollution days with elevated BC mass concentration (exceeding the 95th percentile of concentration for the respective season). These are highlighted in Figure 8. In particular, a total of five cases occurred during the summer months (12 June, 23 June, 30 June, 16 July and 23 August), four cases in the autumn (8 to 10 October and 11 November), five in the winter (23 December, 31 December, 10 January, 12 January and 23 January) and four in the spring (14 to 15 March and 22 to 23 March, which also could be regarded as two cases, each lasting for two consecutive days). It was also observed that these days often coincided with high concentrations of PM10 and NOX. The comparison of the contribution of these days to annual concentrations with non-pollution days showed increases of 9.9%, 10.5% and 12.5% for BC, BCff and BCbb concentrations and 3.5% and 6.9% for PM10 and NOX, respectively. Figure 9 shows that the concentrations of BC, PM10 and NOX on the high-pollution days were almost twice as high as on the non-pollution days. On the non-pollution days, the average concentrations of BC, PM10 and NOx were 0.81, 19.02 and 20.21 μg m−3, while on the high-pollution days they were 2.38, 32.17 and 48.56 μg m−3, which means a threefold increase in BC and a 2.4-fold increase in NOx.
The reasons for increased concentrations and the occurrence of these high-pollution days vary from season to season. Analysing the relationship between BC concentrations, meteorological factors and NOx and PM10 pollution levels during high-pollution and non-pollution days provides valuable insights into the factors influencing air quality variability during all seasons (Table 4).
During the HP days, a negative correlation (from −0.43 to −0.54) between WS and BC concentrations (except during the winter season) suggests an inverse relationship where higher WS are associated with lower BC levels. The winter season shows the situation, where calm WS from the south direction are associated with higher levels of BC (r = 0.56).
In the winter season, in 80% of HP days, there was a positive correlation of BC with PM10 (from 0.26 to 0.57) and NOx (from 0.60 to 0.87), while in 20% of HP days there was a negative correlation of PM10 (−0.11) and NOx (−0.29). During this period, there is also a difference in correlation with WD from negative (from −0.39 to −0.82) to positive (from 0.51 to 0.81). In the summer season, in 60% of cases, there was a very weak positive correlation of BC with PM10 (from 0.09 to 0.18), while in 40% there was a strong positive correlation of PM10 (from 0.45 to 0.57). During the summer case was also a difference in correlation with WD from negative (from −0.21 to −0.39) for 80% and positive (0.38) for 20%. Among these high-pollution days in summer, one recurring day with high concentrations is 23–24 June, which coincides with St. John’s Day (Midsummer) in Lithuania. On this day, the air in Lithuania and some neighbouring countries becomes highly polluted due to wood burning in open bonfires. A two-fold increase in both BC and BCbb concentrations occurred on 23–24 June in contrast to the previous and subsequent days. This is described in more detail in the Minderytė et al., 2023 [46]. Relative humidity and temperature were significant only in the summer and spring periods, and their effects were more than twice as strong on very polluted days, but their association with PM10 differed if they increased in the summer and decreased in the spring. In autumn season only in 50% of case was day was with strong correlation between BC and PM10 (0.64) and WD (−0.53). In the other 50% of the cases no correlation between BC and PM10 (0.12) and WD (−0.19) was observed. Spring-season high-pollution days were associated with the occurrence of illegal grass burning for land clearing in the Kaliningrad region, Ukraine and Belarus as previously were identified [55,56]. This is supported by the presence of smog and active fire data for this period. Figure 9 illustrates a typical case of spring grass burning which was observed on 22 March 2022. Plumes from the wildfires reached the study area, as indicated by the smoke forecast from NAAPS (Navy Aerosol Analysis and Prediction System; http://www.nrlmry.navy.mil/aerosol (accessed on 5 May 2024); see Figure 10c).

4. Conclusions

This study employs a combination of advanced analytical tools and integrated approaches to investigate the temporal dynamics of source-apportioned BC mass concentration in an urban background environment in Vilnius (Lithuania), during the period from 1 June 2021 to 31 May 2022. The study revealed notable seasonal variations in BC mass concentrations, with the annual average concentration of 0.89 µg m−3. Winter exhibited the highest mean BC mass concentration (1.14 µg m−3), followed by spring (1.05 µg m−3), autumn (0.92 µg m−3) and summer (0.55 µg m−3) seasons, demonstrating distinct seasonal patterns. In addition to providing a comprehensive understanding of the seasonal dynamics of BC pollution in the region, the source apportionment analysis revealed the contributions from fossil fuel and biomass burning sources. Notably, the analysis revealed that BCff originating from fossil fuel-related activities consistently dominated throughout the study period, constituting a substantial 71% of the total BC concentration. In contrast, BCbb contributed most significantly during the summer, accounting for 41% of the BC concentration, while its contribution was lowest in autumn, comprising only 23% of the BC concentration. These findings underscore the significant role of fossil fuel in shaping the overall BC pollution.
The study highlighted several key findings regarding high-pollution days in urban background environment. Eighteen such days were identified, characterized by adverse weather conditions in summer, autumn and winter, while spring’s elevated BC levels were linked to grass fires in non-European countries. The changes in correlation patterns between high-pollution and rest days highlighted the complex interactions between variables. During high-pollution days, higher wind speeds were observed to have a more pronounced effect on reducing BC concentrations across all seasons, with the most significant impact occurring in autumn as indicated by the stronger negative correlations of −0.11 and −0.54, respectively. Interestingly, the analysis suggests that wind direction did not play a significant role in relation to BC concentrations during high-pollution days. Furthermore, the data revealed a strong positive correlation between BC mass concentration and NOx throughout the year (r = 0.64), indicating their close relationship. Additionally, BC mass concentration showed a moderate positive correlation with PM10 from autumn to spring. Conversely, BC concentrations exhibited weak to moderate negative correlations with temperature and wind speed during winter and spring seasons. In all seasons, the potential source area was mostly uniformly distributed over the sampling area for all wind directions in calm winds up to 0.5 m s−1. However, the CPF analysis identified an additional source in the south-eastern direction at wind speeds of 1.0–2.5 m s−1 demonstrating the effectiveness of this approach in parallel with wind rose analysis.
This research addresses important gaps in understanding BC pollution in urban environment, offering valuable insights into its spatial and temporal patterns, its seasonal meteorological correlations and its relationship with other air pollutants such as PM10 and NOx. It is important to note, however, that the study’s measurements were taken at a single urban background site, which may limit the broader applicability of the findings.

Author Contributions

Conceptualization, S.B.; methodology, S.B., D.P., A.M., L.D. and V.D.; software, D.P.; validation, S.B., D.P., A.M. and L.D.; formal analysis, D.P., A.M. and L.D.; investigation, S.B., D.P., A.M., L.D. and V.D.; writing—original draft preparation, D.P.; writing—review and editing, S.B., A.M. and L.D.; visualization, D.P.; supervision, S.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author due to privacy.

Acknowledgments

The authors wish to extend their gratitude to the Environmental Protection Agency of Lithuania for generously providing the meteorological and air-pollutant (NOx and PM10) data utilized in this study. We also thank the organizers of the sites for NASA’s Fire Information for Resource Management System (FIRMS; https://firms.modaps.eosdis.nasa.gov/map, accessed on 5 May 2024) and the Navy Aerosol Analysis and Prediction System (NAAPS; http://www.nrlmry.navy.mil/aerosol, accessed on 5 May 2024) for the opportunity to use important information.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Maps of Lithuania, Vilnius and the sampling location.
Figure 1. Maps of Lithuania, Vilnius and the sampling location.
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Figure 2. The time series of BC mass concentration and temperature (the black and blue lines represent the 72-h moving averages) (b) with box plots during the measurement period (a) (the heating season started on 20 September 2021 and ended on 5 May 2022). The colours of the lines and boxes represent the seasons, with summer represented by orange, autumn by red, winter by blue, and spring by green.
Figure 2. The time series of BC mass concentration and temperature (the black and blue lines represent the 72-h moving averages) (b) with box plots during the measurement period (a) (the heating season started on 20 September 2021 and ended on 5 May 2022). The colours of the lines and boxes represent the seasons, with summer represented by orange, autumn by red, winter by blue, and spring by green.
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Figure 3. Seasonal mass concentrations of BC, BCff and BCbb in µg m−3 in Vilnius during the study period. The range of the box depicts the bounds of the 25th and 75th percentiles of the data, while the whiskers extending from the box represent the bounds of the 5th and 95th percentiles, the colour of the box is white for BC, grey for BCff and green for BCbb.
Figure 3. Seasonal mass concentrations of BC, BCff and BCbb in µg m−3 in Vilnius during the study period. The range of the box depicts the bounds of the 25th and 75th percentiles of the data, while the whiskers extending from the box represent the bounds of the 5th and 95th percentiles, the colour of the box is white for BC, grey for BCff and green for BCbb.
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Figure 4. BC mass concentration during the four seasons grouped by day of the week (summer—orange, autumn—red, winter—blue, spring—green).
Figure 4. BC mass concentration during the four seasons grouped by day of the week (summer—orange, autumn—red, winter—blue, spring—green).
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Figure 5. BCff and BCbb mass concentrations during the four seasons: (a) diurnal variation and (b) histograms of relative frequency.
Figure 5. BCff and BCbb mass concentrations during the four seasons: (a) diurnal variation and (b) histograms of relative frequency.
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Figure 6. Bivariate polar plots for hourly BC mass concentration as a function of WS and WD (a,c,e,g) and CPF 75th percentile (b,d,f,h) in the four seasons: summer, autumn, winter and spring.
Figure 6. Bivariate polar plots for hourly BC mass concentration as a function of WS and WD (a,c,e,g) and CPF 75th percentile (b,d,f,h) in the four seasons: summer, autumn, winter and spring.
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Figure 7. Correlation matrix plot between various air pollutants (BC, PM10, NOX) and meteorological parameters (WS, WD, RH, T, Pr) for summer, autumn, winter and spring. The intensity of colour, with red indicating a positive correlation and blue indicating a negative correlation, represents the strength of the relationship between each pair of parameters.
Figure 7. Correlation matrix plot between various air pollutants (BC, PM10, NOX) and meteorological parameters (WS, WD, RH, T, Pr) for summer, autumn, winter and spring. The intensity of colour, with red indicating a positive correlation and blue indicating a negative correlation, represents the strength of the relationship between each pair of parameters.
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Figure 8. The time series of BC, PM10 and air temperature during the four seasons with pronounced days of high air pollution (blue rectangles—periods with hourly BC mass concentration exceeding the 95th percentile of the BC concentration for the respective season).
Figure 8. The time series of BC, PM10 and air temperature during the four seasons with pronounced days of high air pollution (blue rectangles—periods with hourly BC mass concentration exceeding the 95th percentile of the BC concentration for the respective season).
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Figure 9. BC, PM10 and NOx concentrations on non-pollution and high-pollution days.
Figure 9. BC, PM10 and NOx concentrations on non-pollution and high-pollution days.
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Figure 10. Case of spring grass burning observed on 22 March, 2022: (a) active fires (red dots) detected during March 2022 by the MODIS Rapid Response System (each red dot represents a single 1 km MODIS active fire pixel); (b) HYSPLIT back-trajectories of air masses arriving at Vilnius on 22 March 2022 at 500 m (red), 1000 m (blue) and 1500 m (green); (c) smoke surface concentration (µg m−3).
Figure 10. Case of spring grass burning observed on 22 March, 2022: (a) active fires (red dots) detected during March 2022 by the MODIS Rapid Response System (each red dot represents a single 1 km MODIS active fire pixel); (b) HYSPLIT back-trajectories of air masses arriving at Vilnius on 22 March 2022 at 500 m (red), 1000 m (blue) and 1500 m (green); (c) smoke surface concentration (µg m−3).
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Table 1. Descriptive statistics of BC, BCff, BCbb (µg m−3) with standard deviation and BCbb/BC ratio for the season and year.
Table 1. Descriptive statistics of BC, BCff, BCbb (µg m−3) with standard deviation and BCbb/BC ratio for the season and year.
Season BCBCffBCbbBCbb/BC, %
SummerMean (SD)0.55 (0.41)0.32 (0.24)0.23 (0.18)41 (7)
Median0.420.250.1741
Min; Max0.01; 3.430.01; 1.940.01; 1.516; 81
AutumnMean (SD)0.92 (0.83)0.69 (0.61)0.23(0.26)23 (10)
Median0.710.540.1525
Min; Max0.04; 6.620.03; 5.540.01; 2.402; 51
WinterMean (SD)1.14 (1.15)0.78 (0.76)0.35 (0.42)30 (7)
Median0.840.590.2329
Min; Max0.04; 12.330.03; 8.480.01; 5.643; 56
SpringMean (SD)1.05 (1.23)0.75 (0.82)0.31 (0.45)26 (8)
Median0.600.460.1424
Min; Max0.05; 9.010.04; 6.850.02; 3.845; 55
Mean (SD)0.89 (0.99)0.63 (0.67)0.27 (0.35)29 (11)
AnnualMedian0.580.410.1528
Min; Max0.01; 12.330.01; 8.480.01; 5.642.00; 100.00
Table 3. Descriptive statistics of meteorological variables for all seasons.
Table 3. Descriptive statistics of meteorological variables for all seasons.
Seasons Temperature,
°C		Relative Humidity,
%		Barometric Pressure,
hPa		Wind Speed,
m/s
SummerMin6.0259790.10
Mean19.8719940.50
Max35.09710071.57
AutumnMin−7.2349650.10
Mean6.9829960.82
Max28.39710192.70
WinterMin−15.6339600.18
Mean−2.3879891.10
Max6.09710183.27
SpringMin−10.3189620.12
Mean6.3619960.93
Max25.39610263.19
Table 4. Correlation coefficients between the mass concentration of BC and meteorological parameters and air pollution for all seasons during high-pollution (HP) and non-pollution (NP) days.
Table 4. Correlation coefficients between the mass concentration of BC and meteorological parameters and air pollution for all seasons during high-pollution (HP) and non-pollution (NP) days.
Meteorological FactorsSummerAutumnWinterSpring
NPHPNPHPNPHPNPHP
WS−0.35−0.45−0.11−0.54−0.400.56−0.34−0.43
WD−0.092−0.37−0.19−0.19−0.240.44−0.12−0.32
RH0.250.480.0250.250.0670.10.0720.49
T−0.20−0.45−0.190.07−0.230.26−0.27−0.53
P0.0990.22−0.037−0.120.270.310.400.16
PM100.130.280.390.380.570.310.410.24
NOx0.530.570.620.780.630.530.570.55
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Pashneva, D.; Minderytė, A.; Davulienė, L.; Dudoitis, V.; Byčenkienė, S. Understanding the Dynamics of Source-Apportioned Black Carbon in an Urban Background Environment. Atmosphere 2024, 15, 832. https://doi.org/10.3390/atmos15070832

AMA Style

Pashneva D, Minderytė A, Davulienė L, Dudoitis V, Byčenkienė S. Understanding the Dynamics of Source-Apportioned Black Carbon in an Urban Background Environment. Atmosphere. 2024; 15(7):832. https://doi.org/10.3390/atmos15070832

Chicago/Turabian Style

Pashneva, Daria, Agnė Minderytė, Lina Davulienė, Vadimas Dudoitis, and Steigvilė Byčenkienė. 2024. "Understanding the Dynamics of Source-Apportioned Black Carbon in an Urban Background Environment" Atmosphere 15, no. 7: 832. https://doi.org/10.3390/atmos15070832

APA Style

Pashneva, D., Minderytė, A., Davulienė, L., Dudoitis, V., & Byčenkienė, S. (2024). Understanding the Dynamics of Source-Apportioned Black Carbon in an Urban Background Environment. Atmosphere, 15(7), 832. https://doi.org/10.3390/atmos15070832

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