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The concentration of Cr in PC and PF collected in KO and FU was constant, with averages of 114±42 μg/g and 50±32 μg/g in all samples from the two locations, respectively. In contrast, the concentration of Cr in PF collected in SA in summer reached 1,030 μg/g, which was much higher than in the other samples. The high Cr concentration may have resulted from local emissions during the sampling period. Comparison of the results of other samples showed that the concentration of Cr was not higher in PF than in PC. Therefore, this high concentration of Cr was irregular, and the data from this sampling time was removed in subsequent analyses.

The concentrations of Mn in the samples from KO ranged between 399 and 1,330 μg/g and were close to the soil background value (950 μg/g). Mn concentrations in SA and KO samples were similar, but the particle size distributions were different. In KO samples, Mn concentration was higher in PC than in PF, while the opposite was observed in SA samples. Additionally, Mn concentrations for the two particle sizes from both SA and KO were high in winter and spring. Mn concentration was even lower in samples from FU than in those from KO and SA during the same sampling period, especially in samples collected in summer (77.3 μg/g in PF and 59.4 μg/g in PC). Note that not only Mn but the other crustal elements such as Ti was also low in the concentration in aerosol samples collected in summer in FU. One possible speculation for this result is that the Asian dust transportation in summer in Fukuoka may weak in compared to the other seasons (Kaneyasu et al., 2014).

The average concentration of Fe was the highest among the nine metallic elements across the three sites. Fe concentration was the highest in samples from KO (25,100 μg/g), followed by SA (17,300 μg/g) and FU (7,680 μg/g). For samples from the same location, Fe concentration showed trends similar to those of Mn for both particle sizes and particle size concentration distributions.

Cu concentrations in all samples were relatively low except in PC of KO samples (714 μg/g on average). Cu concentrations were comparable in PC and PF in SA (203 vs. 132 μg/g) and FU samples (101 vs. 50.5 μg/g), while there were large differences for KO samples. Cu concentrations in the rest of the samples were higher in winter and spring, except for PF in KO samples.

Zn concentrations in PC and PF from KO samples were considerably different. Trend in Zn concentration were similar to that in Mn, Fe, and Cu. Zn concentration in PC from KO was high (3,440 μg/g) compared with other samples from SA and FU (497 and 235 μg/g, respectively).

Mn(II) was the major component in all aerosol samples at averages of 60.9%, 73.8%, and 85.1% for KO, SA, and FU, respectively. Mn(0) was detected in some PC samples from KO and SA at under 10%. Mn(IV) accounted for a relatively large proportion of aerosol samples collected in KO and SA (37.6% and 25.8% on average), especially in samples collected in winter. These results were generally consistent with a previous study that reported the oxidation state of Mn in the aerosol or road dust samples were approximately 2.2-2.6 (Bardelli et al., 2011; Ohta et al., 2006).

The major valence state of Fe in the aerosol samples was Fe(III). The average proportions of Fe(III) in KO, SA, and FU samples were 70.4% (n=8), 79.8% (n=8), and 86.7% (n=7), respectively. There was more Fe(II) in KO samples (23.5% average) than in SA samples (12.9% on average) and FU samples (8.4% on average). Fe(III) concentration was the highest but Fe(II) concentration was the lowest in FU samples. This may be because Fe(II), which was more reactive valence state than Fe(III), was oxidized during the long-range transport from the Asian continent. This is also supported by Takahashi et al. (2011) which showed that that the ratio of Fe(III) to total Fe increases during atmospheric transport from the Taklimakan desert to Japan. In contrast, there was more Fe(II) in SA and KO samples due to greater local emissions of aerosol particles containing Fe(II). Moreover, Fe(0) was not detected in five samples including PF from KO in autumn and PC from SA in spring. Fe(II) was not detected in PC from SA in summer and PF from FU in autumn.

Cu(II) was the main component of the particles at all locations and sizes. The average percentage of Cu(II) was 92.1% in KO, 88.0% in SA, and 99.4% in FU. The proportion of Cu(0) was larger in PC at KO (13.6% average) and SA (14.4% average), and it was only detected in PF from all FU samples collected in autumn. Cu(I) was not detected in any sample.

Zn(II) was the major chemical valance of Zn in all samples (98.8% in KO, 99.4% in SA, and 97.2% in FU). In order to make more detailed discussion, the chemical speciation results for Zn are presented in Fig. 3. The major Zn components were ZnFe₂O₄ and ZnSO₄. The proportion of ZnFe₂O₄ was higher than ZnSO₄ in all samples (54% vs 31.3% on average) except the fine particles collected in FU site (26.7% vs 65.3% on average). In addition, ZnO existed in all samples except the fine samples collected in spring and summer at FU but at a low amount (12.3% on average). Zn(0) was detected only in some particles and the average proportion in FU samples were three times than others (2.8% to 0.9%). Overall, the Zn chemical speciation result for FU was unique. Note that when we changed ZnSO₄ to Zn(NO₃)₂ as the standard spectrum for the LCF analysis, the result was very similar to the original result.

EFs for metals in the particle samples were calculated by using the average concentrations to investigate the potential sources of metallic elements that were emitted from natural or anthropogenic sources. The results are summarized in Table 2.

The results showed that Ti, V, Cr, Mn, Fe, and Ni had EF values in the range of 0.8-4.4, which suggests that these metals were derived from crustal contribution and part of anthropogenic influences. The EFs of Zn were higher than 10 in all samples at three sites. The values of Pb were also higher than 10 in all samples except for PC collected in the SA site. The EF values of Cu in the KO samples are also higher than 10 and those in the other two sampling sites are also much higher than the reference values as crustal sources. This suggests that the contribution of soil to these elements was much smaller than those of anthropogenic sources. Pb has been used in marker compounds for coal combustion and non-ferrous metal smelters (Ny et al., 2010; Okuda et al., 2008). Additionally, the presence of Pb in atmospheric air is related to vehicular and industrial emissions (i.e., burning of fossil fuels) (Alves et al., 2020; Hassanvand et al., 2015). Xu et al. (2012) listed vehicular emissions as one of the main sources of Pb in the environment of Xi’an, China, even after the use of Pb-containing gasoline was prohibited. Zn is used to make automotive brakes and tire rubber (which contains 1% Zn as it acts an activation agent for the sulfurization of rubber), and Zn is also added to automobile engine oil (Grigoratos et al., 2015; Iijima et al., 2007).

Fig. 3 shows the XAFS results on the valence states of the five trace metals in aerosol particles. The concentration of each valence state was calculated from its percentage and the total metal concentration from Table 2. These values were used in a correlation analysis to investigate the potential emission sources of aerosol particles at the three sites (Table 3). Z-score normalization was applied to all data to standardize different magnitudes into a uniform measure before correlation analysis. In addition, t-tests were performed to determine the statistical significance of the correlation analysis results (Table 3) in order to reduce calculation errors from the small number of samples (n=4). Correlations between an element and its valence states are not discussed as they are not independent, since valence state concentrations were calculated using the element concentrations and valence state percentages.

The concentration of Cr in PF in summer SA samples may be anomalous, as previously discussed. Therefore, we excluded the data for this sampling period to keep the data constant when performing the correlation analysis between Cr and other elements. These correlation coefficients are indicated in Table 3b by a gray background. However, these correlation coefficients are less accurate (e.g., r²=1.00 between Cr(III) and Mn) because of the reduced sample size and will not be used for source analysis. Similarly, the correlation analysis of elements in FU PC samples was excluded because of the lack of EDXRF and XAFS data on FU PC samples collected in winter.

As shown in Table 3, there were significant correlations between Mn(IV) and Ti, and the correlation of Fe(II) and Fe(III) with Ti and Mn for all samples. Ti, Mn, and Fe are all typical crustal enrichment elements, and the EFs for both Mn and Fe were close to 1 (Table 2), which indicates that Mn and Fe mainly originated from the soil. There is research pointed that small-sized particles were emitted from vehicle exhausts as a result of engine combustion (Tanner et al., 2008), and Lu et al. (2009) stated that the principal source of Mn and Pb in street dust is automotive emissions. Therefore, the high correlations of Mn with Cu, Zn, and Pb in SA fine particles indicated that automotive emissions might be one anthropogenic source of Mn.

Zn is the largest metallic portion emitted from electric arc furnace dust, which can reach 7%-40% of total metal emissions. Vapor zinc is emitted out from the furnace as compounds like ZnO and ZnFe₂O₄ with other gaseous or particulate compounds generated during steelmaking reactions (Oustadakis et al., 2010; Xia and Picklesi, 2000), while Mn is derived from franklinite ((Zn, Mn²⁺, Fe²⁺) (Fe³⁺, Mn³⁺)₂O₄), and Cr is richer in the dust from stainless steelmaking than in that from carbon steelmaking (Omran and Fabritius, 2017; Havlík et al., 2006; Sofilić et al., 2004). Manno et al. (2006) stated that heavy metals such as Zn and Ni are highly abundant in urban areas and industrial sites, and major sources of V have been identified as combustion of residual fuel such as ships and refinery plants (Agrawal et al., 2008a, 2008b; Vallius et al., 2003). Therefore, the strong correlations of Zn, Mn, Fe, and Cr with Ni (r²=0.801-0.975, p<0.05) and V (r²=0.814-0.965, p<0.05) in aerosol particles at KO and FU sites indicates that they were likely emitted from industrial production in the Keihin and Kitakyushu industrial areas, where also are important ports.

Cu and Zn are known as wear-generating elements, as Cu can be emitted from the wear of engine parts, fuel, and oil leakage (Hassan, 2012; Yuen et al., 2012), while Zn can be derived from tire abrasion, brake wear, and corrosion of vehicle parts (López et al., 2011). Previously deposited Pb in the soil may be re-suspended in road dust (Taylor and Kruger, 2020; Chen et al., 2019; Jeong et al., 2018; Amato et al., 2014; Chen et al., 2014). This explains the significant correlations between Cu(II), Zn(II), and Pb. Furthermore, a previous chemical characterization study of brake wear particles showed that Fe existed in both fine and coarse fractions, while TEM analysis revealed the presence of maghemite (γ-Fe₂O₃), magnetite (FeO-Fe₂O₃), and amorphous carbon in the nanoparticle fraction, as well as maghemite, magnetite, and hematite (α-Fe₂O₃) in the fine fraction (Grigoratos et al., 2015). Hence, the Fe produced by brake wear emissions from cars is mainly in the form of compounds. However, the correlation of Fe(II) and Fe(III) with Zn and Cu was not statistically significant due to the limitation of sample numbers in this study, so this anthropogenic contribution of Fe deserves further investigation.

Table 4 shows the principal components (PC) of the valence states of target metals from XAFS. Eigenvalues, variance contributions, and cumulative variance are also presented. We also performed CA on target metal compounds for the three sites to better identify possible emission sources (Fig. 4). We performed PCA and CA for both particle sizes together because the number of samples was limited and because the irregular Cr concentration in PF collected in summer at SA had to be removed. The PCA and CA results for the three sites were different, even for KO and SA, which were relatively close geographically. This indicates that the emitting sources at each site were different and depended on their geographical location and regional characteristics.

Fig. 4. 
Cluster analysis of target metal compounds from (a) KO, (b) SA, and (c) FU.

For KO, the PCA model extracted three PCs and explained up to 94% of the total variance. PC1 and PC2 contained the most pollutant components, as their combined cumulative variance contribution reached 89.0%. The components originally belonging to PC1 were grouped into one cluster with those of PC2 in the CA analysis results. As previous discussion of emission sources of included factors, PC1 indicated the surface dust emission sources, including Cu(II) and Zn(II) generated from traffic brake wear, Mn(II), Fe(II), and Fe(III) from crustal soil. In combination with the CA results, Fe(II) and Fe(III) were also classified into the same cluster with Cr(0) and Mn(IV) in PC2, this means possible anthropogenic activities emitting such as fly ash generated from steel and marine industries in the Keihin industrial area. The PCA and CA results show that Cr(III) was unique, it was always grouped separately and the variance contribution was 5.2% for PCA. There were strong correlations of Cr(III) with V, Ni, Cu, and Pb (Table 3a). Ni and V are often considered to be tracers of ship emissions because both are major impurities in residual oil used by oceangoing vessels, and shipping facilities are considered an important source of airborne fine Ni and V in port areas (Tian et al., 2013; Peltier et al., 2009), while Cu and Pb are the main elements in fly ash from municipal waste incinerators, and the crystalline and non-crystalline forms of Cr and Cu can be incorporated into the spinel structure in fly ash particles (Dahlan et al., 2020; Takaoka et al., 2016; Jung et al., 2004; Wiles, 1996). Therefore, PC3 containing only Cr(III) can be considered to represent the combustion source such as fossil fuel burning and waste incineration.

At SA, the overall variance contribution of the two PCs was 95.1%. The PCA and CA results for the valence states were also consistent. This indicates that the sources of pollutants in this region were relatively stable, and the emission sources derived from the model results are sufficient to identify the largest sources of the target contaminants. The components of PC1 for SA and KO were similar. However, since Saitama Prefecture is an inland-type region with more developed agricultural production, the valence distribution of Mn and Fe, which mainly originate from the soil, maybe more related to the solidification process of toxic metals, which occurs in paddy soils during rice cultivation. Manganese oxides can alter the valence states of other heavy metals, such as arsenic (Oscarson et al., 1983a, 1983b). For example, the reduction of Mn(IV) to Mn(II) oxidizes As(III) to As(V), and oxidation of As(III) by Mn oxides likely leads to decreased As mobility in soil (Lafferty et al., 2010a, 2010b). Mn oxides would also promote Fe(II) oxidation to Fe(III), thereby creating freshly precipitated Fe(III) hydroxides that could serve as efficient sorbents for As (Xu et al., 2017; Ehlert et al., 2016). Some Mn-containing pesticides also contribute to the PM-bound Mn besides the soil re-suspension (Sysalova et al., 2012). This might explain why the highest concentrations of Mn(II) and Fe(III) were found in SA samples in summer and autumn when rice harvesting can cause more soil dust to enter the atmosphere. PC2 including the Cr(0), Cr(III), and Fe(0) might indicate the industrial emission source around the sampling site, and/or abrasion of metallic alloys on surfaces.

For FU, the PCA results were complex. For example, Fe(0) and Cu(II) had relatively high loadings in different PCs, and their PCA and CA results differed significantly. This was probably due to the diverse industrial structure of the Fukuoka region and its proximity to the East Asian continent, which makes it susceptible to long-distance transport of pollutants from the continent. Therefore, the sources of pollutants are more complex. The small number of samples (n=7) obtained at FU may be another reason behind the large differences between the PCA and CA results. PCA is designed to capture the maximum amount of raw information and reduce the number of variables by extracting the main components of multivariate data, while CA groups variables that are close together into the same cluster in the dendrogram.

In terms of source identification of contaminants at FU, Cu(II) and Zn(II) were likely emitted from traffic brake wear. Cr(III), Mn(II), Mn(IV), Fe(II), and Fe(III) in aerosol particles were likely influenced by fly ash generated by steel production in the Kitakyushu industrial area, as previously discussed. Furthermore, the high correlation of Mn(II), Mn(IV), and Fe(III) with Ti indicate that they may have been from peri-urban agricultural production emissions. The CA results showed that Cr(0) and Fe(0) belonged to separate clusters, with low correlations between them and other pollutants but they were grouped in PC4 with Cu(II) and Zn(II) by PCA result. Kennedy and Gaddy (2003) presented that brake dust generated during the braking process wear of the brake discs/drums, which are commonly made of steel of alloys was enriched in Cr, Cu, Fe, and tin (Sn). This might indicate the unique sources for Cr(0) and Fe(0), such as the abrasion of metallic alloys on surfaces.