Seasonality of the Airborne Ambient Soot Predominant Emission Sources Determined by Raman Microspectroscopy and Thermo-Optical Method

: Raman microspectroscopy and thermo-optical-transmittance (TOT) method were used to study airborne ambient soot collected at the suburban air monitoring station in southern Poland during the residential heating (January-February) and non-heating (June–July) seasons of 2017. Carbonaceous material constituted on average 47.2 wt.% of PM 2.5 during the heating season and 26.9 wt.% in the non-heating season. Average concentrations of OC (37.5 ± 11.0 µ g/m 3 ) and EC (5.3 ± 1.1 µ g/m 3 ) during the heating season were signiﬁcantly higher than those in the non-heating season (OC = 2.65 ± 0.78 µ g/m 3 , and EC = 0.39 ± 0.18 µ g/m 3 ). OC was a chief contributor to the TC mass concentration regardless of the season. All Raman parameters indicated coal combustion and biomass burning were the predominant sources of soot in the heating season. Diesel soot, which is structurally less ordered than soot from other sources, was dominant during the non-heating season. The D1 and G bands area ratio (D1 A /G A ) was the most sensitive Raman parameter that discriminated between various soot sources, with D1 A /G A > 1 for diesel soot, and less than 1 for soot from coal and wood burning. Due to high daily variability of both TOT and Raman spectroscopy data, single-day measurements can be inconclusive regarding the soot source apportionment. Long-time measurement campaigns are recommended.


Introduction
Carbonaceous matter (CM) is a common and important component of atmospheric aerosols, and accounts for 20-50% of the total aerosol mass [1]. CM consists of primary carbon-rich particles and of both primary and secondary organic compounds [2]. The primary carbon-rich particles are interchangeably called soot, black carbon (BC), or elemental carbon (EC). However, the term elemental carbon refers to thermally-refractory carbon and should be used only in conjunction with chemical or thermo-optical analyses [3][4][5]. Black carbon is a descriptive term of light-absorbing CM in atmospheric aerosol [6] that should be used only in reference to optical properties of CM. BC is the strongest light absorber per unit mass of all atmospheric aerosol species [7]. Due to the strong absorption of light combined with its omnipresence in the atmosphere, ambient BC is an important climate forcer [7]. BC should not be confused with carbon black, which is an industrial carbon material widely used in numerous applications [8].
Soot is the solid, carbon-rich material originating from incomplete combustion of fossil fuels and biomass burning. Soot is composed of fractal-like aggregates of spherical particles, usually smaller than 100 nm, built of concentrically wrapped, graphene-like The mean annual concentrations of airborne PM2.5 range from 20 to 35 μg/m 3 within about 90% of the Racibórz city limits; whereas, in the remaining part of the city, these concentrations are higher, up to 45 μg/m 3 [30]. Diurnal concentrations of PM10 and PM2.5 in the winter heating season can be as high as 184 and 161 μg/m 3 , respectively, due to coal combustion for domestic purposes [30]. Little difference exists in poor air quality in winter between the Racibórz city center and the air monitoring station at the city's outskirts, as suggested by the number of days, 54 and 43, respectively, with exceedances of the recommended value for the average daily PM2.5 concentration (25 μg/m 3 ) [31]. The average PM2.5 concentrations in the heating season (late October-late April) and non-heating season of 2019 measured at suburban stations were 35 (5-136) μg/m 3 and 17 (4-65) μg/m 3 , respectively [31]. The mean seasonal concentrations of PM2.5 recorded at the station located in the city center were comparable and amounted to 36 (6-134) μg/m 3 (heating season) and 16  μg/m 3 (non-heating season).
There is a distinct seasonality of air pollution in Silesia caused by large-scale coal burning in winter [32]. Soot is the principal constituent of PM in winter, with maximum concentrations exceeding 90% vol.; whereas, in summer, the soot content is in the 5-10% vol. range [14].

Sample Description
Four reference soot samples were collected from an exhaust pipe of a diesel-fueled car (sample E), two different wood-burning fireplaces (samples W1 and W2), and a coalburning home furnace (sample C). These are typical soot sources in the Silesia province and soot emitted from these sources is expected to predominate in ambient air. Ambient soot together with PM2.5 were collected on quartz filters (Whatman QMA) using a Low Volume Sampler (LVS) with the volumetric flow rate set to 2.3 m 3 /h. Eight samples were collected during the heating season (January and February) and another 8 samples during the non-heating season (June-July) of 2017. The adopted division into these two specific The mean annual concentrations of airborne PM 2.5 range from 20 to 35 µg/m 3 within about 90% of the Racibórz city limits; whereas, in the remaining part of the city, these concentrations are higher, up to 45 µg/m 3 [30]. Diurnal concentrations of PM 10 and PM 2.5 in the winter heating season can be as high as 184 and 161 µg/m 3 , respectively, due to coal combustion for domestic purposes [30]. Little difference exists in poor air quality in winter between the Racibórz city center and the air monitoring station at the city's outskirts, as suggested by the number of days, 54 and 43, respectively, with exceedances of the recommended value for the average daily PM 2.5 concentration (25 µg/m 3 ) [31]. The average PM 2.5 concentrations in the heating season (late October-late April) and non-heating season of 2019 measured at suburban stations were 35 (5-136) µg/m 3 and 17 (4-65) µg/m 3 , respectively [31]. The mean seasonal concentrations of PM 2.5 recorded at the station located in the city center were comparable and amounted to 36 (6-134) µg/m 3 (heating season) and 16  µg/m 3 (non-heating season).
There is a distinct seasonality of air pollution in Silesia caused by large-scale coal burning in winter [32]. Soot is the principal constituent of PM in winter, with maximum concentrations exceeding 90% vol.; whereas, in summer, the soot content is in the 5-10% vol. range [14].

Sample Description
Four reference soot samples were collected from an exhaust pipe of a diesel-fueled car (sample E), two different wood-burning fireplaces (samples W1 and W2), and a coalburning home furnace (sample C). These are typical soot sources in the Silesia province and soot emitted from these sources is expected to predominate in ambient air. Ambient soot together with PM 2.5 were collected on quartz filters (Whatman QMA) using a Low Volume Sampler (LVS) with the volumetric flow rate set to 2.3 m 3 /h. Eight samples were collected during the heating season (January and February) and another 8 samples during the non-heating season (June-July) of 2017. The adopted division into these two specific periods, commonly used in the literature, was dictated primarily by differences in air temperatures and the resulting difference in heat demand and its consumption [31,32].

Raman Spectroscopy
Raman spectra were collected in the 100-4500 cm −1 range using a WITec alpha 300R confocal Raman microspectrometer equipped with an air-cooled solid-state laser and a CCD camera. The best quality spectra were obtained using the 488 nm excitation line. The excitation laser radiation was introduced into the microscope through a single-mode optical fiber with a diameter of 3.5 µm. Microscope objectives LD EC Epiplan-Neofluan DIC-100/0.75NA; LD EC Epiplan-Neofluan DIC-50/0.55NA, and EC Epiplan-1/0.25NA were used. Raman scattered light was focused onto a single-mode optical fiber (30 µm in diameter) and onto a monochromator with a 600 mm −1 diffraction grating. Raman spectra were accumulated by 10 scans with a single spectrum acquisition time of 10 s and a resolution of 3 cm −1 . All Raman spectra were recorded during a single session under the same operating conditions because the laser beam power on the sample could not be measured.
Baseline correction and cosmic ray removal were performed using WITec Project software (version 4,1,12). Peak fitting was performed using the IFORS (Iterative Fitting of Raman Spectra) software [33]. Only the first order spectra in the range of 400 to 2200 cm −1 were used for the fitting procedure.
The automatic modeling of the background in IFORS is based on the fifth-order polynomial and the pseudo-Voigt (PV) functions. The sum of the functions and the polynomial is adjusted to the spectrum by randomly changing a single parameter (center, height, HWHM). When the parameters are optimized, the model adds more PV functions until the maximum number of PV functions is reached. Based on the sum of optimized PV functions, an accurate, corrected, and smoothed spectrum baseline is obtained. In subsequent mathematical processes, the scaled total area of the D (D STA ) and G (G STA ) bands is calculated [33]. IFORS is a software package dedicated to the study of carbonaceous substances and has been successfully used in earlier studies [34,35]. Statistical analyses were performed using the Stat Soft software package, Statistica 12.0.

Thermal-Optical Transmittance (TOT) Analysis
Organic carbon (OC) and elemental carbon (EC) in PM 2.5 were determined using a Sunset Laboratory Inc. thermal-optical analyzer (Model 4L Main Oven Assembly) with flame ionization detector (FID). In TOT analysis, the carbonaceous component of PM collected on quartz-fiber filters evolves thermally during combustion at stepwise increases in temperature in accordance with the EUSAAR-2 temperature protocol (Table S1). The protocol was recommended as a standard method for European monitoring stations under the European Supersites for Atmospheric Aerosol Research (EUSAAR) project [4]. The procedure applied in this study is described in detail in [31]. The 1.0 cm 2 section of each quartz filter with deposited PM 2.5 was gradually heated in the inert atmosphere of He to release four temperature-resolved organic carbon fractions (OC1-4). Then, the filter sections were heated in an oxidizing helium-oxygen mixture to release four elemental carbon fractions (EC1-4). The carbon-bearing compounds released from the filter during the stepwise heating were converted to CO 2 in an oxidizer oven in the presence of MnO 2 catalyst. The CO 2 was then reduced to methane in the methanator and its amount was measured by FID. During the inert mode of the analysis (heating in He) some organic compounds easily pyrolize into a refractory material called pyrolitic carbon (PC), which resembles EC [4]. During TOT analysis, the pyrolytic conversion of OC into PC is continuously monitored by measuring the transmission of a laser beam through the filter and the results are automatically corrected. After the correction for PC, the OC is obtained as: OC = OC1 + OC2 + OC3 + OC4 + PC; whereas EC = EC1 + EC2 + EC3 + EC4 − PC [31]. The total carbon (TC) content is the sum of OC and EC.
The measurement performance was controlled by regular split point determination for the pyrolytic carbon, in addition to systematic analysis of blank filters and calibration of the apparatus-periodic analysis of a calibration (control) sample of glucose solution of known concentration. In addition, the auto-calibration of the analyzer was conducted through the inclusion of a fixed volume loop used to inject an external standard (Air Liquide) at the end of each analysis. The total uncertainties in EC and OC measurements were of the order of 5% (at a 95% confidence interval). The limits of detection (LOD) were 0.17 and 0.80 µg/cm 2 , respectively, for EC and OC. Moreover, certified reference materials (CRMs; RM 8785 and RM 8786; National Institute of Standards and Technology (NIST)) were periodically analyzed-recovery of 90-102% and 98-124% was obtained, respectively, for OC and EC.

Mass Concentrations of PM 2.5 , OC and EC
The mass concentration of PM 2.5 collected during the heating season ranged from 65.29 to 119.08 µg/m 3 (mean value 90.65 ± 20 µg/m 3 ) and was much higher than that in non-heating season samples (7.5-14.5 µg/m 3 ; mean value 11.025 ± 2.44 µg/m 3 ) ( Table S2). The high variability of PM 2.5 concentrations, particularly in the heating season, most probably reflects changes in daily meteorological conditions. Nevertheless, PM 2.5 mass concentrations throughout the sampling campaign in winter exceeded the daily limit value  (Table S2). A significant increase in the concentration of CM in cold months is a common phenomenon in Poland due to widespread use of coal for household heating and cooking [36][37][38]. Poland, however, is not exceptional in this respect. Seasonal variability of carbonaceous aerosols with a peak mass concentrations in winter caused by human activities is commonly observed in countries with residential heating based on biomass burning and/or coal combustion, e.g., [39][40][41][42][43][44][45][46].
There is a strong positive correlation between OC and EC for the entire measurement periods and for the heating season (R 2 = 0.94); whereas, in the non-heating season the correlation is weak (R 2 = 0.51) ( Figure 2). A strong relationship between OC and EC suggests a dominant primary source of carbonaceous matter [39].
for the pyrolytic carbon, in addition to systematic analysis of blank filters and calibration of the apparatus-periodic analysis of a calibration (control) sample of glucose solution of known concentration. In addition, the auto-calibration of the analyzer was conducted through the inclusion of a fixed volume loop used to inject an external standard (Air Liquide) at the end of each analysis. The total uncertainties in EC and OC measurements were of the order of 5% (at a 95% confidence interval). The limits of detection (LOD) were 0.17 and 0.80 μg/cm 2 , respectively, for EC and OC. Moreover, certified reference materials (CRMs; RM 8785 and RM 8786; National Institute of Standards and Technology (NIST) were periodically analyzed-recovery of 90-102% and 98-124% was obtained, respec tively, for OC and EC.

Mass Concentrations of PM2.5, OC and EC
The mass concentration of PM2.5 collected during the heating season ranged from 65.29 to 119.08 μg/m 3 (mean value 90.65 ± 20 μg/m 3 ) and was much higher than that in non-heating season samples (7.5-14.5 μg/m 3 ; mean value 11.025 ± 2.44 μg/m 3 ) (Table S2) The high variability of PM2.5 concentrations, particularly in the heating season, most proba bly reflects changes in daily meteorological conditions. Nevertheless, PM2.5 mass concentra tions throughout the sampling campaign in winter exceeded the daily limit value of 25 μg/m 3 (Table S2). A significant increase in the concentration of CM in cold months is a common phenomenon in Poland due to widespread use of coa for household heating and cooking [36][37][38]. Poland, however, is not exceptional in this respect. Seasonal variability of carbonaceous aerosols with a peak mass concentrations in winter caused by human activities is commonly observed in countries with residentia heating based on biomass burning and/or coal combustion, e.g., [39][40][41][42][43][44][45][46].
There is a strong positive correlation between OC and EC for the entire measuremen periods and for the heating season (R 2 = 0.94); whereas, in the non-heating season the cor relation is weak (R 2 = 0.51) (Figure 2). A strong relationship between OC and EC suggests a dominant primary source of carbonaceous matter [39].  OC was a chief contributor to the TC regardless of the season (Table S2) and on average constituted 87.63 wt.% of TC during the heating season and 87.53 wt.% in summer samples. The average OC/EC ratio for samples from the heating season (7.02 ± 1.30) was slightly lower than that for the non-heating season (7.44 ± 2.05). However, individual OC/EC values varied significantly daily during both sampling periods (Table S2). The high OC/EC values in the heating season suggest an increased role of residential biomass burning in addition to coal combustion because fossil fuel burning is characterized by lower OC/EC values, which are often below unity [40]. The predominance of OC over EC in PM regardless of the season has been observed in several sites of southern Poland and was attributed to biomass burning throughout the year [31,32].
In addition to the band positions, the following Raman parameters were applied in the present study: the D and G bands scaled total area (D STA and G STA ) as defined in [33]; the D and G bands scaled total area ratio (D STA /G STA ); the D1 and G bands area ratio (D1 A /G A ); the Raman area ratio (RAR) [33]; Raman band separation (RBS = G − D); the D and G bands height intensity ratio (I D /I G ); the D1 and G bands height intensity ratio (I D1 /I G ); and the G-band full width at half maximum (FWHM G ). The D-band FWHM for all samples was 120 cm −1 ; hence, it was not useful for discriminating soot from different seasons.

Reference Samples
Unprocessed Raman spectra of all reference samples consist of two broad and overlapping G and D bands that are typical of disordered carbon (Figure 3). The D band centers in the 1340-1380 cm −1 range and the G band in the 1580-1600 cm −1 range. Both bands in the spectrum of diesel soot (Figure 3d) are of almost equal maximum intensity, unlike in the other spectra, in which the G band is stronger than the D band (Figure 3a-c). The increase in the D-band intensity is considered to be a sign of increased disorder in the graphitic-type structure [13]. The diesel soot spectrum was deconvoluted into four broad band components (D1, D3, D4, and G). The spectra of soot from wood burning are more complex and were deconvoluted into five band components (D1, D3, D4, D5, and G) (Table 1; Figure 3a,b). The D5 band observed in the spectra of soot from both wood and coal combustion were probably caused by vibrations of aliphatic hydrocarbons [51,52]. In the spectrum of soot originated from coal combustion, there is an additional weak and very broad band centered at 1303 cm −1 , designated D6 (Figure 3d). The D6 band can be attributed to hydrocarbons trapped in micropores of carbonaceous material [53].
The D2 component commonly observed as the shoulder on the G band at around 1620 cm −1 and attributed to structural disorder [13] was not resolved during the curve fitting in this study. It is possible that the low crystallinity of the investigated samples, and the resulting low G-band intensity, did not allow the relatively weak D2 band to be distinguished from the G band, as suggested by Beyssac et al. [54].
The Raman shift of D and G bands and the band separation are not specific for any type of the reference soot (Table 1). They are therefore not useful as indicators of the source of soot in the atmospheric air. Other parameters are considerably more sensitive in this respect ( Table 2). The D2 component commonly observed as the shoulder on the G band at around 1620 cm −1 and attributed to structural disorder [13] was not resolved during the curve fitting in this study. It is possible that the low crystallinity of the investigated samples, and the resulting low G-band intensity, did not allow the relatively weak D2 band to be distinguished from the G band, as suggested by Beyssac et al. [54].
The Raman shift of D and G bands and the band separation are not specific for any type of the reference soot (Table 1). They are therefore not useful as indicators of the source of soot in the atmospheric air. Other parameters are considerably more sensitive in this respect ( Table 2). Table 1. Curve-fitting results of Raman spectra (cm −1 ) of the reference soot from wood burning (W1 and W2), coal-fired furnace (C), and tail-pipe diesel exhaust (E). D1  D2  D3  D4  D5  D6  G  W1  1356  1589  1380  -1494  1196  1294  -1586  W2  1367  1595  1364  -1499  1192  1278  -1594  C  1337  1598  1382  -1502  1238  1303  1174  1594  E  1353  1595  1353  -1472 1218 --1589 * band shifts measured in the unprocessed spectra.     The height intensity ratio of D and G bands (I D /I G ) is a sensitive measure of soot crystallinity, i.e., the size of graphitic domains [7,13]. The I D /I G ratio depends on numerous factors, including aromatic layer size, soot oxidation reactivity, maturation of coals, aging of diesel soot at various temperature, and fuel to oxygen ratio ( [24] and references therein). The I D /I G ratio is close to unity (0.97) for the reference diesel soot; whereas, soot from other sources show significantly lower I D /I G ratios (0.67-0.70) ( Table 2). The diesel soot spectrum is characterized by a similar height intensity of the D and G bands (Figure 3d), which was not observed for the other spectra, in which the height intensity of the D band is significantly lower than that of the G band. This observation is consistent with previous investigation that showed a high amorphous to graphitic carbon ratio in diesel soot [24,55]. Diesel engine soot may have a higher degree of disorder (high amorphous to graphitic carbon ratio) compared to gasoline soot [24]. The four component bands observed in the spectrum of the reference diesel soot (Figure 3d) have been also reported in other studies [55][56][57][58][59]. Therefore, it can be considered that this is a characteristic feature of diesel soot. Some authors, however, distinguished three component bands in Raman spectra of both diesel and gasoline soot [24,49].

Sample D * G *
The I D /I G ratio of soot from coal combustion (0.67) is only slightly lower than that of soot originating during wood burning (0.69-0.70) ( Table 2).
The FWHM G of diesel soot (96 cm −1 ) is significantly higher than that of soot from other sources, except for sample W1 ( Table 2). There is an inverse relation between the FWHM of the Raman bands and the degree of crystallinity of CM [13,25,27,29]. There is a noticeable difference in FWHM G of soot originating from burning of different types of wood. Apparently, the crystallinity of the W1 soot is lower than that of W2.
The most distinct differences in Raman spectra of diesel soot and soot from other sources are revealed by the D1 A /G A, D STA /G STA , and RAR parameters ( Table 2). The D STA /G STA ratio is higher in the spectra of soot from wood and coal-burning than diesel soot. The D STA /G STA ratio increases with increasing order in the structure of CM [60]. The D1 A /G A value (1.57) of diesel soot is between 2.7 and 3 times higher than that for wood-and coal-derived soot ( Table 2). The D1 A /G A ratio decreases with the increase in the degree of order in the carbonaceous material [61]. The D1 A /G A ratio most distinctly differentiates between poorly ordered diesel soot (D1 A /G A > 1) and soot form other sources (D1 A /G A << 1).
All of the Raman parameters are indicative of low crystallinity of the reference diesel soot compared to coal and wood soot, most probably as a result of higher content of amorphous carbon in the former.

Ambient Soot
Raman spectra of ambient soot collected during the heating season (Figure 4a and Figure S1) are similar in shape to those of the reference soot originating from coal and biomass burning (Figure 3a-c); whereas, Raman spectra of soot collected during the non-heating season (Figure 4b and Figure S1) match the reference spectrum of poorly ordered diesel soot (Figure 3d). These similarities are further confirmed by all of the Raman parameters (Table S3). The D1 A /G A and RAR parameters show the most distinct differences between samples from both seasons despite high variability of the daily data ( Figure 5).      The RAR parameter for samples from the heating season was in the range of 2.10 4.02, and the D1A/GA ratio ranged from 0.57 to 0.96 (on average 0.77 ± 0.18). The compari son of these values with data for the reference material (Table 2) unequivocally points t soot derived from coal and wood (biomass) as being a predominant component of th investigated samples. The RAR for samples from the non-heating season was in the rang of 0.74-1.97 (average 1.02 0.42 , whereas the D1A/GA ranged from 1.06 to 1.96 (averag 1.53 ± 0.30). These values are very close to those of diesel soot (Table 2).
Raman spectra of samples from the heating season are characterized by significantl weaker and broader D band than G band (Figure 4). The D band has a much larger area ranging from 267 to 295, compared to the G band (170-199) ( Table S3). The opposite rela tions between the D and G bands were observed in the Raman spectra of samples from the non-heating season (Figure 4 and Table S3). The D band is less pronounced and it area is in the range of 192-257 comparable to the G band (174-226) making the two main bands similar in size and shape. Average values of G-band area are similar for soot from heating (186) and non-heating (183) seasons (Table S3).
The DSTA/GSTA ratio is distinctly higher for samples from the heating season (1.41 1.68, on average 1.53 ± 09) than for the non-heating season (1.09-1.24, on average 1.13 The RAR parameter for samples from the heating season was in the range of 2.10-4.02, and the D1 A /G A ratio ranged from 0.57 to 0.96 (on average 0.77 ± 0.18). The comparison of these values with data for the reference material (Table 2) unequivocally points to soot derived from coal and wood (biomass) as being a predominant component of the investigated samples. The RAR for samples from the non-heating season was in the range of 0.74-1.97 (average 1.02 ± 0.42), whereas the D1 A /G A ranged from 1.06 to 1.96 (average 1.53 ± 0.30). These values are very close to those of diesel soot ( Table 2).
Raman spectra of samples from the heating season are characterized by significantly weaker and broader D band than G band ( Figure 4). The D band has a much larger area, ranging from 267 to 295, compared to the G band (170-199) ( Table S3). The opposite relations between the D and G bands were observed in the Raman spectra of samples from the non-heating season ( Figure 4 and Table S3). The D band is less pronounced and its area is in the range of 192-257 comparable to the G band (174-226) making the two main bands similar in size and shape. Average values of G-band area are similar for soot from heating (186) and non-heating (183) seasons (Table S3).
The D STA /G STA ratio is distinctly higher for samples from the heating season (1.41-1.68, on average 1.53 ± 09) than for the non-heating season (1.09-1.24, on average 1.13 ± 04) and is similar to the D STA /G STA ratio for soot originating from coal combustion (1.47) and wood burning (1.44-1.47); whereas, values for the non-heating samples are close to the D STA /G STA ratio of diesel soot (1.06).
The mean I D /I G ratio of soot collected during heating season (0.66) is close to the reference soot from both coal combustion (I D /I G = 0.67) and wood burning (I D /I G = 0.69); whereas the mean I D /I G of soot collected during the non-heating season (0.86) suggests the prevalence of diesel engine exhaust (I D /I G = 0.97 in Table 2) with a probable contribution from gasoline engine exhaust. Gasoline soot has a lower D/G intensity ratio compared to diesel soot [24].
The average value of FWHM G for the heating season soot (87 ± 6 cm −1 ; median 83 cm −1 ) is significantly lower than for the summer samples (97 ± 12 cm −1 ; median 99 cm −1 ). Individual values of FWHM G deviate much more significantly from the average than those of I D /I G (Table S3).
In the heating season, the I D /I G ratio follows the changes in FWHM G , whereas during the non-heating season, both parameters behave differently. This observation is confirmed by Spearman's rank correlation (α = 0.05). The correlation between I D /I G and FWHM G was positive (r = 0.50) during the entire measurement period and was particularly strong (r = 0.85) during the heating season. In the non-heating season the correlation was lower and statistically insignificant (r = 0.43). The correlation analysis shows that, as the degree of disorder in the soot structure increases, the correlation between the I D /I G and FWHM G weakens.

Relations between Raman I D /I G Parameter and TOT Data
A correlation matrix between I D /I G and PM 2.5 , OC, EC, and TC was computed for the entire measurement period and separately for heating and non-heating seasons (Table S4). In the entire measurement period, the highest negative correlation occurred between I D /I G and PM 2.5 (r = −0.92). Strong negative correlations were also recorded between I D /I G and OC, EC, and TC (r = −0.91). Considering the OC and EC thermally evolved fractions, high correlations between I D /I G and EC3, OC3, and OC4 were observed (r = 0.92, 0.91 and 0.90, respectively).
Correlations between I D /I G and OC, EC, TC, and PM 2.5 in the heating season were statistically significant (r = −0.78, r = −0.76, r = −0.78 and r = 0.71, respectively) but weaker than correlations for the entire sampling period. For the thermally evolved fractions of the OC and EC components, the highest correlation was between I D /I G and EC3 (r = −0.81). A high correlation (r from −0.71 to −0.78) was also observed for EC2, EC1, and OC3. For other components, the correlations were statistically insignificant. In the non-heating season, a statistically significant correlation occurred only between I D /I G and EC4 (r = −0.71).
Statistical analyses showed that there is a high correlation between the degree of order in the soot structure and the content of individual carbon fractions, which is particularly noticeable throughout the entire measurement period. In this case, very high I D /I G correlations were observed for both OC and EC. The highest correlations were between I D /I G and EC3 and between I D /I G and OC3, whereas the lowest was between I D /I G and EC4. The distribution of correlations suggests that the soot throughout the entire measurement period originated from sources with the temperature range of 450-700 • C. During the heating season, the correlations between I D /I G and OC and EC were lower.
In the non-heating season, a statistically significant correlation occurred only between I D /I G (0.77-0.98) and EC4 (r = −0.74), which suggests the influence of vehicular transport as a major soot source.
Raman spectroscopy cannot distinguish between coal-combustion soot and soot originating from wood burning in ambient air samples; alternatively, other methods, namely determination of biomass burning tracers, i.e., levoglucosan, OC, OC/EC, and K, should be used.
Results obtained during this study are not entirely unexpected. The soot emission from coal combustion for domestic purposes in Silesia is extremally high in winter compared to vehicular soot emission, e.g., [37]. Moreover, vehicular traffic in Poland is about 20% lower in winter than in summer [62]. Apparently, the occurrence of diesel soot in winter samples is masked by the abundant soot that originates during incomplete coal combustion and biomass burning. The decline in coal combustion for domestic purposes in summer results in a relative increase in the amount of airborne diesel soot. However, there is still some contribution from biomass and coal burning, as reflected in the lower I D /I G ratio than expected for diesel soot. There may also be some contribution of the gasoline soot to Raman spectra of CM. Although this option was not explored during this study, it requires further consideration.

Conclusions
Raman parameters can reliably be used for distinguishing between diesel soot and soot from coal and wood combustion in ambient air samples. The D1 A /G A ratio is the most sensitive parameter, with values higher than 1 for diesel soot and much lower than 1 for soot from both coal and wood burning. However, other Raman parameters also consistently indicated that coal combustion and biomass burning were the predominant sources of soot in the heating season. Diesel soot, which is structurally less ordered than soot from other sources, was dominant during the non-heating season. The strongest negative correlation occurred between I D /I G and the highest temperature (850 • C) fraction EC4 for soot from the non-heating season, which confirms results of Raman spectroscopy that indicate vehicular transport is a major soot source.
Strong correlations between the coefficients obtained from both methods confirm the high efficiency of Raman spectroscopy as a method compatible with the TOT method, which is commonly used in air quality research.
Due to the high daily variability of both TOT and Raman spectroscopy data, single-day measurements can be inconclusive regarding the soot source apportionment. Long-time measurements are recommended for this purpose.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/atmos12060768/s1, Figure S1: Raman spectra of soot in PM 2.5 collected in Racibórz during heating (H) and non-heating (NH) seasons. Table S1. EUSAAR-2 temperature protocol used in thermal-optical-transmittance analysis; Table S2. Mass concentrations (µg m −3 ) of PM 2.5 ; Table S3. Raman parameters obtained from the deconvolution of D and G bands in the spectra of soot collected during heating and non-heating seasons in Racibórz; Table S4. Nonparametric (Spearman) correlation matrix (α = 0.05) for ID/IG and concentrations of OC, EC (including their thermally evolved components), TC, pyrolitic product (Pyr), and PM 2.5 (µg·m −3 ) calculated for the entire sampling period, and separately for the heating and non-heating seasons.