Ⅲ. RESULTS AND DISCUSSION
3.2 Profiles of PCNs in food
3.2.1 Homologue profiles
The mean proportions of measured PCNs in food to the total concentrations for each food group are shown in Figure 8. Among the six homologues, tri-CN was predominant in all three groups, followed by tetra-CN and penta-CN in fishery products and processed foods, while penta-CN and tetra-CN were second and third common in agricultural products. This is because CN-24 was the most abundant congener in the food samples. The ratio of tri-CNs to total PCNs was highest in processed foods (68%), followed by fishery products (55%) and agricultural products (34%). The sum of tri-CNs to penta-CNs made up large proportions– 83% in agricultural products, and 98% in fishery products and processed foods. By contrast, the uptake of hepta-CNs and octa-CN was much lower than that of lower chlorinated naphthalenes due to their strong binding to particles, limiting absorption into biota (Helm et al., 2008;
Opperhulzen et al., 1985). In a previous study on PCNs in sediments and biota, hepta-CNs and octa- CN were dominant in sediments, while these were not observed in the bodies of blue crabs (Kannan et al., 1998).
Figure 8. (a) Mean concentrations and (b) fractions of the PCNs in food groups.
0%
20%
40%
60%
80%
100%
Agricultural products
Fishery products
Processed foods 100
80
60
40
20
0 0 20 40 60 80
Agricultural products
Fishery products
Processed foods
(a) Concentration (b) Fraction
Fraction (%)
Concentration (pg/g ww)
Octa-CN Hepta-CN
Hexa-CN Penta-CN
Tetra-CN Tri-CN
22
It should be noted that there have been few studies that have analyzed tri-CN in food. The distribution of PCNs in this study is similar to previous studies on rice and wheat in Pakistan (Mahmood et al., 2014) and fisheries in Spain (Llobet et al., 2007) and China (Cui et al., 2018), which concluded that tetra-CN and/or penta-CN were the predominant homologues from tetra-CN to octa-CN. In a study on fishery products with tri-CN to octa-CN, tri-CN and penta-CN were dominant homologues (Jiang et al., 2007).
Earlier studies on various environmental matrices, including air (Xue et al., 2016), soil (Mahmood et al., 2014), and sediment (Lundgren et al., 2002), also found similar distribution patterns. In a few studies of human-related samples, tetra-CN and penta-CN are the major contributors in human serum (Park et al., 2010), while it was penta-CN and hexa-CN in breast milk (Pratt et al., 2013) and adipose tissue (Schiavone et al., 2010) with highest contributions from CN-66/67.
Figure 9 shows the homologue distribution of the PCNs in each food items. Most of the agricultural products, milk, and red chili paste showed slightly different homologue profiles to the other products.
In particular, the profiles of chestnuts and milk were quite different due to the detection of few congeners. Meanwhile, the contributions from hepta-CN were slightly higher in watermelons and peaches. These results might have been influenced by their protective skins, as mentioned in the concentration part. The total concentrations in salmon and crucian carp were similar, but the two have different distributions of homologues. Salmon was dominated by penta-CN with high concentrations of CN-52/60, 50, 51, and 59. By contrast, crucian carp was highly contributed by tri-CN and tetra-CN.
Salmon is one of the higher trophic level organisms, whereas crucian carp belong to bottom feeders.
Other fish, such as the fine-spotted flounder and pond loach, showed similar homologue patterns to that of the crucian carp. Similar results with high contributions from penta-CNs and hexa-CNs in higher trophic level organisms were observed in several previous studies (Gewurtz et al., 2018; Hanari et al., 2004; Helm et al., 2008).
23
Figure 9. (a) Mean concentrations and (b) fractions of the PCNs in food samples in agricultural products, fishery products, and processed foods.
Fraction (%)Concentration(pg/gww)
(a) Concentration
(b) Fraction
Processed foods Fishery products
Agricultural products
0 50 100 150 200 250
0 20 40 60 80
100 Agricultural products Fishery products Processed foods
Octa-CN Hepta-CN
Hexa-CN Penta-CN
Tetra-CN Tri-CN
24
3.2.2 Correlations among PCNs, PCDD/Fs and DL-PCBs
The relationships among PCN homologue groups were identified by Spearman correlation analysis (Table 5). Tri-CNs, tetra-CNs, penta-CNs and hexa-CNs were correlated with each other, whereas hepta-CNs and octa-CN showed less or no correlation with other homologue groups. This result indicates that the different uptake pattern of hepta-CNs and octa-CN to other homologues. As discussed above, CN-73, 74, and 75 (hepta-CNs and octa-CN) less bioaccumulate since they are susceptible to photodegradation due to steric interactions of substituted chlorines in 1,8- and 4,5- positions (Gewurtz et al., 2009; Helm et al., 2008). Less accumulation of hepta-CNs and octa-CN in biota was observed in many previous studies (Byer et al., 2013; Cui et al., 2018; Domingo et al., 2003; Llobet et al., 2007).
Table 5. Spearman correlations among PCN homologue groups.
Tri-CNs Tetra-CNs Penta-CNs Hexa-CNs Hepta-CNs Octa-CN
Tri-CNs 1
Tetra-CNs .741** 1
Penta-CNs .413** .751** 1
Hexa-CNs .208** .517** .652** 1
Hepta-CNs -.263** -.268** -.216** -.065 1
Octa-CN .162* .130 .148* .228** -.015 1
**Correlation is significant at the 0.01 level (2-tailed) *Correlation is significant at the 0.05 level (2-tailed).
The relationships among PCN congeners, PCDD/F congeners, and DL-PCB congeners in food were also examined (Table 6). Considering the detection frequency and contribution to Σ55 PCN concentrations, CN-20 and CN-24 from tri-CNs, CN-37/33/34 from tetra-CNs, CN-52/60 and CN-51 from penta-CNs, and CN-66/67 from hexa-CNs were selected. The data for PCDD/Fs, DL-PCBs in food were taken from a Ministry of Food and Drug Safety report (MFDS, 2016), and data in chestnut, Korean sausage, and mayonnaise were excluded due to the absence of data of PCDD/Fs and DL-PCBs.
PCN congeners except for CN-51 and CN-66/67 showed strongest correlations to each other (r = 0.448–
0.962, p < 0.01), followed by the correlation between PCN congeners and DL-PCB congeners (r = 0.311–0.806, p < 0.01), and the weakest between PCN congeners and PCDD/F congeners except for octachlorodibenzo-p-dioxin (OCDD) (r = 0.081–0.576, p < 0.01). No correlation between PCNs and OCDD was also observed in previous study (Kim et al., 2018).
Among PCN congeners, CN-37/33/34 is a technical mixture-related congener, and CN-52/60, CN-51, CN-66/67 are combustion-related congeners (Lee et al., 2007). These congeners were correlated with DL-PCBs or PCDD/Fs excluding OCDD, or both. This result could indicate that the exposure route of PCNs was similar with those of PCDD/Fs and DL-PCBs in food.
25
Table 6. Spearman correlations among major congeners of PCNs, PCDD/Fs, and DL-PCBs in food samples.
CN-20 CN-24 CN- 37/33/34
CN-
52/60 CN-51 CN-
66/67 ΣPCNs OCDD 2,3,7,8- TCDF
2,3,4,7,8 -PeCDF
ΣPCDD /Fs
PCB 77
PCB 105
PCB 118
PCB 126
CN-24 .962** 1
CN-37/33/34 .737** .806** 1
CN-52/60 .500** .596** .758** 1
CN-51 .105 .248 .376 .549** 1
CN-66/67 .331 .393* .448* .728** .157 1
ΣPCNs .867** .935** .915** .738** .458* .448* 1
OCDD .282 .321 .361 .271 .433* .081 .355 1
2,3,7,8-TCDF .304 .371 .431* .475* .387* .173 .413* .585** 1 2,3,4,7,8-
PeCDF .429* .476* .553** .576** .536** .274 .535** .650** .738** 1
ΣPCDD/Fs .459* .505** .514** .535** .417* .301 .525** .677** .918** .852** 1
PCB 77 .567** .598** .622** .778** .311 .621** .640** .374 .564** .591** .631** 1
PCB 105 .494** .540** .607** .806** .385* .652** .613** .346 .615** .638** .670** .938** 1
PCB 118 .524** .556** .583** .766** .359 .602** .611** .378 .653** .673** .710** .943** .982** 1
PCB 126 .509** .562** .695** .769** .360 .627** .652** .374 .603** .690** .653** .841** .900** .872** 1 ΣDL-PCBs .529** .571** .682** .765** .338 .596** .655** .371 .601** .694** .653** .882** .921** .912** .984**
**Correlation is significant at the 0.01 level (2-tailed).
*Correlation is significant at the 0.05 level (2-tailed).
26
3.2.3 Principal component analysis (PCA)
PCA was conducted to understand the relationships between distribution patterns of PCN homologues and food samples. In addition to the PCN homologue profiles in agricultural products, fishery products, and processed foods, homologue profiles in PCN technical mixtures (Halowaxes) were used for the analysis (Figure S2). Concentrations of each homologue were divided by the total PCN concentrations for the normalization. These normalized PCN concentrations were used as input data for PCA.
Figure 10. Scatter plot of PCA results: (a) score and (b) loading plots for PCN homologues and Halowaxes. The outliers (chestnut and HW1051) were not shown in the plot (coordinate: 0.28, 3.80 and 0.15, 4.21).
The results of PCA are shown in Figure 10. In the loading plot, principal component 1 (PC 1), explaining 36% of the total variance, have positive PC loading values for tetra- to octa-CN and negative values for tri-CNs. Proximity between penta- and hexa-CNs, and hepta- and octa-CN showed the correlation between them. In the score plot, most of the agricultural products have positive PC 1 scores, indicating that agricultural products were much more correlated with tetra- to hexa-CNs. As discussed earlier, agricultural products had higher proportion of tetra-CNs to hexa-CNs (55%) than the other food groups (45% in fisheries, 32% in processed foods). Meanwhile, negative PC 1 loading values were related to most of the processed foods excluding milk, red chili paste, and ramen. Fishery products except for salmon were mostly located at the middle part between the processed foods and agricultural products.
Regardless of food groups, more than half food items (fishery products excluding salmon, processed foods without milk and red chili paste, apple, peach, rice, and sweet potato in agricultural products) seemed to be clustered around Halowax 1000, featured by tri-CNs and tetra-CNs. By contrast, milk, radish, and onion, which had high PC 1 values, were correlated with Halowax 1014, and featured by penta-CNs and hexa-CNs.
Onion Radish Salmon HW1000 Milk
HW1001 HW1013
HW1014
HW1031 HW1099
-3.0 -1.5 0.0 1.5 3.0
-3.0 -1.5 0.0 1.5 3.0
Agricultural Fishery Processed Halowaxes
Tri Penta
Hexa Hepta
Octa
Tetra
-1.0 -0.5 0.0 0.5 1.0
-1.0 -0.5 0.0 0.5 1.0
Component 1 (36 %)
Component 2 (26 %)
(a) (b)
27
3.3 TEQ
The estimated TEQ values of PCNs in food are shown in Figure 11. TEQ, calculated with REPs from previous study (Falandysz et al., 2014), were statistically higher than those with REPs from other studies (ANOVA test, p < 0.05) (Behnisch et al., 2003; Suzuki et al., 2020). In order to minimize under- estimations, REP values with the highest TEQ values (Falandysz et al., 2014) were used for calculation of the dietary intakes. There were REP values for only 20 congeners among the 55 PCN congeners.
The Σ20 TEQPCN ranged from 0.0001 to 0.0017 pg-TEQ/g ww in agricultural products, 0.0004 to 0.0102 pg-TEQ/g ww in fishery products, and 0.0000 to 0.0037 pg-TEQ/g ww in processed foods. The sum of Σ20 TEQPCN was the highest in fishery products (0.0185 pg-TEQ/g ww), which is a similar result to those in previous studies (Zacs et al., 2020). The highest Σ20 TEQPCN were observed in salmon with a 0.0102 pg-TEQ/g ww, followed by butter (0.0037 pg-TEQ/g ww) and crucian carp (0.0037 pg-TEQ/g ww). The high total concentrations of PCNs in salmon and crucian carp contributed to the high TEQ values. Otherwise, the high TEQ value in butter was affected by the contribution of CN-66/67, which has the highest REP value among congeners. Several studies found similar TEQ levels in salmon in Korea (0.014 pg-TEQ/g ww; Kim et al. (2018)), salmon and butter in UK (salmon: 0.02 pg-TEQ/g ww and butter: 0.01 pg-TEQ/g ww; Fernandes et al. (2010)), and common carp in Great Lakes, Canada (80 pg-TEQ/g ww; Gewurtz et al. (2018)). Although coffee showed the highest total Σ55 PCNs, its Σ20
TEQPCN was not as high as others due to the lower REP values of its dominant congeners (Table S3).
In congener profiles of Σ20 TEQPCN, CN-66/67 was predominant in fishery products (34%) and processed foods (58%), whereas it was CN-73 in agricultural products (29%). For the mean Σ20 TEQPCN
of all food items, CN-66/67 (38%) was the most predominant congener, followed by CN-69 (16%), CN-63 (12%) and CN-64/68 (12%). These congeners were all in the hexa-CN homologue, accounting for 77% of the total Σ20 TEQPCN. This fraction was consistent with the other studies on fish in China (hexa-CNs: 63%; Cui et al. (2018)) and in Great Lakes, Canada (CN-66/67: 76–80%; Gewurtz et al.
(2018)), and on various food groups in Latvia (hexa-CNs: 80%; Zacs et al. (2020)). The fraction of CN- 66/67, CN-69, and CN-63 was also large in butter and cheese in China (Li et al., 2015).
The proportions of concentrations of the TEQ values of PCNs, PCDDs, PCDFs, and DL-PCBs to the combined TEQ were 1.3%, 5.6%, 28.5%, 64.6%, respectively. The contribution of Σ20 TEQPCN to the combined TEQ was much lower than those of PCDD/Fs and DL-PCBs. Earlier studies also found similar fractions of PCNs, which were <15% in Gewurtz et al. (2018), 5% in Li et al. (2015), and 0–
12% in Kim et al. (2018). However, in a previous study, the fraction of Σ TEQPCN was 26.8% of the combined TEQ in human serum, which was similar with those of PCDDs, PCDFs and DL-PCBs (Park et al., 2010).
28 Figure 11. TEQ values of PCNs in food samples.
0.000 0.002 0.004 0.006 0.008 0.010 0.012
Tetra-CN Penta-CN Hexa-CN Hepta-CN Octa-CN
Concentration (pg-TEQ/g)
Agricultural products Fishery products Processed foods
a b
c
aBehnisch et al. (2003)
bFalandysz et al. (2014)
cSuzuki et al. (2020)
29
3.4 Dietary intake
3.4.1 Dietary intake of PCNs
In order to lessen underestimate of dietary intake of PCNs, MFDS data for 8 livestock products and 20 fishery products were added to calculation (MFDS, 2019b). The concentration of 11 congeners (CN-52, 53, 66, 68, 69, 71, 72, 63, 73, 74, and 75) from additional data are listed in Table S4. To avoid misinterpretation of the data due to the difference in the number of congeners, Σ11 TEQPCN or Σ20 TEQPCN
were used depending on the purpose of data interpretation. Σ11 TEQPCN were used for the comparison of the “proportions” of the intake from food items or food groups to the total intake of PCNs. Otherwise, Σ20 TEQPCN were used for the estimated “values” of the intake of PCNs.
Based on the food consumption survey (KNHANES) conducted by KHIDI, the average daily consumption of 496 food items for overall average age group was 1505.17 g/day (KHIDI, 2019). With the absence of consumption data of pheasant meat, the daily food consumption of 57 selected food in this study was 658.5 g/day, which accounted for 44% of the total consumption. In addition, the considered food categories (agricultural products, fishery products, livestock products, and processed foods) cover 74% of the total. Daily food consumptions of 9 age groups (1–2, 3–5, 6–11, 12–18, 19–
29, 30–49, 50–64, over 65, and overall average) were listed in Table S5.
Figures 12 to 16 depict the contributions of food items to the total intake of Σ11 TEQPCN, the intake from agricultural products, fishery products, livestock products, and processed foods, respectively. Each figure consists of pi-charts for overall average and 8 age groups.
The results of earlier studies were referred for each food group. However, it is important to note that direct comparison was impossible due to the limited number of studies, difference of congeners, homologue groups, food items and categories, and based on whether TEQs are applied.
30
Figure 12. Distributions of dietary intake (pg-TEQ/kg bw/day) of Σ11 TEQPCN in selected food samples.
The contributions of eleven PCN congeners in each food item to the total intake are depicted in Figure 12. The intakes from oyster, webfoot octopus, beef, sesame oil, and soybean oil were zero, due to the influence of TEQ values. For the overall average group, the total intake of Σ11 TEQPCN from 57 food items was 0.392–0.823 pg-TEQ/day. Previous studies reported the total intake of tetra-CNs to octa-CN as 0.104 ng/kg bw/day in Spain (Martí-Cid et al., 2008), and the total intake of 26 congeners as 0.036 pg/kg bw/day in Latvia (Zacs et al., 2020).
The highest contribution to the total intake corresponded to pork (27–35%) for people between 6 and 49 years old, whereas rice made the biggest contribution (23–31%) for the youngest (1–5 years) and the oldest (≥ 50 years), due to the very high consumption of pork (12.67–75.37 g/day; mean 50.21 g/day) and rice (97.3–171.1 g/day; mean 145.9 g/day) among the Korean population. In spite of the high Σ11TEQPCN, the contributions of sailfin sandfish (2.7×10-5 pg-TEQ/kg bw/day), rockfish (8.9×10-5 pg- TEQ/kg bw/day), Spanish mackerel (4.4×10-5 pg-TEQ/kg bw/day), and salmon (7.5×10-5 pg-TEQ/kg bw/day) were relatively low due to the low consumption of these fish in Korea.
Pork 25%
Rice 22%
Anchovy 9%
Radish 5%
Duck meat 5%
Egg, 4%
Onion, 3%
Ramen, 3%
Rice 28%
Anchovy 17%
Pork Yogurt 12%
6%
Egg 5%
Milk 5%
Hairtail, 4%
Pork 27%
Rice Duck 24%
meat 6%
Anchovy 6%
Egg, 5%
Ramen, 4%
Milk, 3%
Pork 33%
Rice 19%
Anchovy 7%
Ramen 5%
Radish, 5%
Egg, 4%
Onion, 4%
Pork 29%
Rice 20%
Anchovy 8%
Duck meat 5%
Radish 5%
Egg, 4%
Onion, 3%
Rice 23%
Pork 19%
Anchovy 12%
Radish 7%
Egg, 4%
Duck meat 3%
Onion, 3%
Hairtail, 3%
Rice 31%
Anchovy Pork 13%
12%
Radish 7%
Duck meat 5%
Hairtail, 4%
Onion, 3%
Pork 35%
Rice Duck 21%
meat 7%
Ramen 6%
Egg, 4%
Anchovy, 3%
Rice 25%
Pork 17%
Anchovy 13%
Duck meat 8%
Yogurt 6%
Egg, 5%
1–2 3–5
6–11 12–18
30–49 65
19–29 50–64
Overall average
31
Figure 13. Distributions of dietary intake (pg-TEQ/kg bw/day) of Σ11 TEQPCN in agricultural products.
Figure 13 shows the contribution of Σ11 TEQPCN in each food item to the intake from nine agricultural products. The sum of the intake of Σ11 TEQPCN from these agricultural products ranged from 0.157 to 0.299 pg-TEQ/day. A previous study in Spain reported the intake of tetra-CNs to octa-CN was 0.006 ng/kg bw/day from vegetables, and 0.004 ng/kg bw/day from fruits (Martí-Cid et al., 2008).
For the overall average age, the highest intake of Σ11 TEQPCN from agricultural products corresponded to rice (63%), followed by radish (15%) and onion (9%), which is due to the high consumption of rice and onion, and the high TEQs of radish. This result was similar across most of the Korean population except for 1–2 years old, whose intake came from rice, radish and apple. Unlike radish and onion, the consumption of apple for 1–2 year-olds is not significantly different from that of overall average, hence apple is third place for 1–2 year-olds. The sum of the intake from these three items accounted for 82.5–
90.3% of the intake from 9 agricultural products.
Rice 63%
Radish 15%
Onion 9%
Rice 70%
Radish 10%
Apple 6%
Rice 69%
Radish 7%
Onion 6%
Rice 70%
Onion 8%
Radish 8%
Rice 69%
Radish 10%
Onion 9%
Rice 63%
Radish 16%
Onion 12%
Rice 61%
Radish 15%
Onion 11%
Rice 59%
Radish 18%
Onion 8%
Rice 66%
Radish 14%
Onion 6%
1–2 3–5
6–11 12–18
30–49 65
19–29 50–64
Overall average
32
Figure 14. Distributions of dietary intake (pg-TEQ/kg bw/day) of Σ11 TEQPCN in fishery products.
Figure 14 depicts the contributions of 28 fishery products to the intake of Σ11 TEQPCN. The total intake of Σ11 TEQPCN from fishery products was 0.077–0.191 pg-TEQ/day. The intakes from these products were 0.012 pg-TEQ/kg/day in Latvia (Zacs et al., 2020), 0.022–0.028 ng/kg bw/day in Spain (Llobet et al., 2007; Martí-Cid et al., 2008), and 0.44 pg-TEQ/day for tetra-CNs to octa-CN in Korea (Kim et al., 2018).
Anchovy significantly contributed (35–70%) to the dietary intakes from fishery products due to the wide use of anchovy-based broth in various dishes in Korea. Hairtail and mackerel were also responsible for high intakes of Σ11 TEQPCN due to the high TEQ of hairtail and high consumption of mackerel. These results match the results of a previous study on fisheries in Korea (Kim et al., 2018). The top three contributors accounted for 87.4–88.2 % of the intake from fisheries among young children (1–5 years old), and 56.9–67.8% in adults (19–49 years old). This means more items affected the intake from fisheries for adults.
Anchovy 46%
Hairtail 11%
Mackerel 7%
Small octopus 6%
Crab 6%
Anchovy 63%
Hairtail 16%
Mackerel 9%
Anchovy 70%
Mackerel 9%
Hairtail 8%
Anchovy 42%
Hairtail 17%
Mackerel 10%
Spanish mackerel
8%
Anchovy 35%
Crab Small 13%
octopus 11%
Hairtail 11%
Spanish mackerel
7%
Anchovy 43%
Hairtail Small 12%
octopus 8%
Squid, 6%
Mackerel 5%
Anchovy 42%
Crab Squid8%
7%
Hairtail 7%
Small octopus 7%
Mackerel 7%
Anchovy 48%
Hairtail 13%
Mackerel 8%
Crab 6%
Anchovy 53%
Hairtail 15%
Mackerel 8%
1–2 3–5
6–11 12–18
30–49 65
19–29 50–64
Overall average
33
Figure 15. Distributions of dietary intake (pg-TEQ/kg bw/day) of Σ11 TEQPCN in livestock products.
The contributions of seven livestock products to the intake of Σ11 TEQPCN are given in Figure 15. The total intake of Σ11 TEQPCN from livestock products was 0.076–0.368 pg-TEQ/day. According to the overall average group, the highest contribution corresponded to pork (75%), followed by duck meat (14%), and eggs (11%). The high intakes of pork and eggs were influenced by high consumption, 50.21 g/day and 29.53 g/day, respectively. By contrast, the high intake of duck meat was affected by its high TEQ, which was three times higher than that of pork. These items accounted for more than 99% of the total intake from livestock products regardless of the age of the consumer. Although other items, including beef and chicken meat, have high consumption, their contributions to the intake of PCNs were much lower, or even zero, due to their TEQ values. A previous study in Latvia reported the intakes from meat and meat products as 0.003 pg-TEQ/kg bw/day, and from eggs as 0.0002 pg-TEQ/kg bw/day for 26 PCN congeners (Zacs et al., 2020). The intakes of tetra-CNs to octa-CN from meat and meat products was 0.006 ng/kg bw/day in Spain (Martí-Cid et al., 2008).
Pork 75%
Duck meat 14%
Egg
11% Pork
62%
Egg 27%
Duck meat
11%
Pork 56%
Duck meat
27%
Egg 17%
Pork 71%
Duck meat
17%
Egg 12%
Pork 76%
Duck meat
16%
Egg 8%
Pork 85%
Egg 10%
Duck meat
5%
Pork 61%
Duck meat
25%
Egg 14%
Pork 76%
Duck meat
14%
Egg 10%
Pork 73%
Egg 14%
Duck meat
13%
1–2 3–5
6–11 12–18
30–49 65
19–29 50–64
Overall average