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Metal levels in fodder, milk, dairy products, and tissues sampled in ovine farms of Southern Italy
ARTICLE IN PRESS
Environmental Research 99 (2005) 48–57 www.elsevier.com/locate/envres
Metal levels in fodder, milk, dairy products, and tissues sampled in ovine farms of Southern Italy$ Rosa Caggianoa, Serena Sabiaa, Mariagrazia D’Emiliob, Maria Macchiatob, Aniello Anastasioc, Maria Ragostad,, Salvatore Painoe a
IMAA, Istituto di Metodologie per l’ Analisi Ambientale CNR, Tito Scalo (PZ), Italy b INFM, Dipartimento di Scienze Fisiche, Universita` Federico II, Napoli, Italy c Dipartimento di Scienze Zootecniche e Ispezione degli Alimenti, Universita` Federico II, Napoli, Italy d INFM, Dipartimento di Ingegneria e Fisica dell’ Ambiente, Universita` della Basilicata, Via dell’Ateneo Lucano, 85100 Potenza, Italy e Dipartimento di Scienze della Produzione Animale, Universita` della Basilicata, Potenza, Italy Received 28 June 2004; received in revised form 29 October 2004; accepted 8 November 2004 Available online 25 December 2004
Abstract We measured Cd, Cr, Hg, Mn, and Pb levels in samples of fodder, milk, dairy products, and tissues collected from 12 ovine farms in the regions of Campania and Calabria (Southern Italy). The areas in which the farms are located show different levels of anthropogenic pressure. The main purpose of this study is the identification and the analysis of relationships among metal concentrations observed in samples representative of different links in the food chain. Particularly, we apply univariate, bivariate, and multivariate statistical techniques to identify the correlation structure of our data set and to evaluate the influence of anthropogenic activity. We discuss the results, focusing the analysis on the spatial and the temporal patterns of metal concentrations. r 2004 Elsevier Inc. All rights reserved. Keywords: Heavy metals; Ovine; Milk; Dairy products; Tissues
1. Introduction In the higher organisms, the intake of trace elements occurs mainly through the respiratory system or through the food chain. Particularly, many dangerous elements or compounds, such as dioxins, pesticides, metals, and metalloids, accumulate along the food chain. In this way, the food chain becomes the main gateway for persistent toxicants to enter higher organisms. Furthermore, these compounds generally have an anthropogenic origin, and thus their concentrations in $ This work was supported by grants from INFM (Istituto Nazionale per la Fisica della Materia) Biophysics Section funds. Any studies involving experimental animals were conducted in accordance with national and institutional guidelines for the protection of animal welfare. Corresponding author. Fax: +39 971 205169. E-mail address: [email protected] (M. Ragosta).
0013-9351/$ - see front matter r 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.envres.2004.11.002
the environment grow with the increase of urban, agricultural, and industrial emissions. In this framework, the control of the levels of these elements in food is not only an important aspect of food quality but also an indirect monitoring system of anthropogenic activity impacts on soil, water, and air (Albers et al., 2000; Rajaratnam et al., 2002; Burger et al., 2002; Bratakos et al., 2002; Toso et al., 2002; Alberti-Fidanza et al., 2003; Chen et al., 2003; Prankel et al., 2004). We focus this study on heavy metals. Their concentrations (bioavailable or total content) in the environment are generally used as an early indicator of contamination phenomena both in programs of soil quality control and in air quality monitoring (Caggiano et al., 1998; Ragosta et al., 2002; Koc- ak et al., 2004; Loska et al., 2004). Particularly, direct correlations between the concentrations of these elements in air or in soil and the increase of anthropogenic emissions have
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been observed, not only on large spatial–temporal scales, but also over short periods and in small areas. The heavy metal levels are examined also in biomonitoring procedures that are based on vegetal accumulators and that evaluate environmental stress in urban and industrial areas (Cuny et al., 2001; Caggiano et al., 2004). We measured Cd, Cr, Hg, Mn, and Pb levels in samples of fodder, milk, dairy products, and tissues from ovine farms. The study was carried out in 12 farms located in six zones of the regions of Campania and Calabria (Southern Italy) that are characterized by different levels of anthropogenic activity impact. In the literature, we find studies dealing with heavy metal concentrations in milk, in dairy products, or in tissues (Sivertsen et al., 1995; Peyrovan and Amirabadi, 1996; Lopez-Alonso et al., 2000; de Souza-Lima et al., 2002; Sedki et al., 2003; Licata et al., 2004; Merdivan et al., 2004), but only in a few cases (Coni et al., 1996, 1999) has a set of metals been measured in different kinds of samples collected in farms spread over a large area. Furthermore, these studies highlight that it is difficult to individuate well-defined relationships among heavy metal concentrations in animal feed, milk, diary products, and tissues. In fact, animal nutrition affects the hygienic quality of milk, but it is not easy to control and to evaluate contamination coming from industrial processes, agricultural practices, and atmospheric depositions. Moreover, some studies put in evidence that the manufacturing processes may influence heavy metal concentrations in diary products (Coni et al., 1996; Nardone and Valfre`, 1999). In this context, the main purpose of this work is the identification and analysis of the relationships among the metal concentrations observed in different kinds of samples representative of different links in the food chain. Particularly, we analyze the spatial and temporal patterns of metal concentrations in order to point out a relationship among metal levels observed in ovine food and metal levels observed in milk and in dairy products. Analogously, we analyze the relationships among metal concentrations measured in fodder samples and metal concentrations measured in different tissues (liver, muscle, kidney, and fat) commonly considered target organs (Sedki et al., 2003). Finally, we investigate also the spatial distribution of metals measured in different samples, taking into account the anthropogenic pressure in the areas surrounding the farms.
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Fig. 1. Study area. The farms are located in the regions of Campania and Calabria.
regions of Southern Italy. The map of the sampling sites is shown in Fig. 1. We describe the geographical features and the anthropogenic characteristics of the sampling sites using two aggregated indices (Table 1), which are defined as follows: Index G takes into account the altitude (Q) and the distance from the sea (D), which well identify the microclimatic features of the area (Macchiato et al., 1995). In particular, GM indicates the mountainous inner sites [Q values more of than 700 m above sea level (a.s.l.) and D values of more than 30 km], GH the hill sites (Q values in the range of 300–600 m a.s.l. and D values in the range of 10–25 km), and GC the coastal sites (Q values of lower than 300 m a.s.l. and D values of lower than 10 km). Index P is a cumulative index that takes into account the presence of different emission sources (high volume of traffic, industrial plants, dumps, waste incinerators, metropolitan areas, small-medium towns, villages, intensive agricultural activities); it ranges from 0 (for zones with the lowest anthropogenic pressure) to 7 (for the zones with the highest anthropogenic pressure). 2.2. Sampling protocols
2. Experimental data and methods 2.1. Sampling sites All of the samples were collected during Summer 2001 and Winter 2002 in 12 ovine farms located in two
We analyzed samples of fodder, milk, ricotta, cheese (8 days of maturation), mature cheese (60 days of maturation), liver, muscle, kidney, and fat. The samples were collected during two different periods: July 2001 (summer samples are labeled s01) and January–March
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50 Table 1 Parameters for farm characterization Site label
AV1 AV2 AV3 AV4 SA1 KR1 KR2 NA1 KR3 KR4 CE1 CE2
Region
G index
Campania Campania Campania Campania Campania Calabria Calabria Campania Calabria Calabria Campania Campania
P index
GM GM GM GM GC GC GC GH GC GC GH GH
T
InP
D
Inc
sUS
mUS
lUS
Sum
0 0 0 0 1 1 1 1 1 1 0 0
0 0 1 0 1 1 0 1 0 0 0 0
0 0 0 0 1 1 1 0 0 0 0 1
0 0 1 0 0 0 0 0 0 0 0 0
0 0 1 1 1 1 1 1 1 1 0 0
0 0 0 0 1 1 1 1 1 1 0 0
0 0 0 0 0 0 0 1 0 0 0 0
0 0 3 1 5 5 4 5 3 3 0 1
T, traffic; InP, industrial plants; D, dumps; Inc, incinerators; sUS, small urban sites; mUS, medium urban sites; lUS, large urban sites; GM, mountainous inner sites; GH, hill sites; GC, coastal sites; Sum ¼ P index value. Table 2 Available samples for each sampling site Site label
AV1 AV2 AV3 AV4 SA1 KR1 KR2 NA1 AV2 KR3 KR4 CE1 CE2
Sampling label
s01 s01 s01 s01 s01 w02 w02 w02 w02 w02 w02 w02 w02 N
Available samples Fodder
Milk
Ricotta
Cheese
Mature cheese
’ ’ ’ ’ ’ ’ ’ ’ 13
’ ’ ’ ’ ’ ’ ’ ’ 12
’ ’ ’ ’ ’ ’ ’ ’ 10
’
’ ’ ’ ’ ’ ’ ’ 10
’ ’ ’ ’ ’ 8
Liver
Muscle
Kidney
Fat
’
’
’
’
’
’
’
’
3
4
4
4
, summer; ’, winter; s01, summer 2001; w02, winter 2002; N, sample number. 2002 (winter samples are labeled w02). The sampling plan is shown in Table 2. All of the samples were collected according to Italian norms and following rigorous procedures in order to minimize possible external contaminations. In particular, each milk sample comprised seven takings of milk during the morning milking. In eight of the 12 farms, the milk samples were divided in three parts; the first was analyzed as milk, the second was used to produce ricotta samples, and the third was used to produce cheese and mature cheese samples. 2.3. Trace elements analysis The elemental analysis of Cd, Cr, Mn, and Pb was performed using a Varian AA200 atomic absorption spectrophotometer equipped with a graphite furnace.
A microwave system was used for the acid digestion of all of the samples. The fodder samples were dried at 70 1C for 48 h in a forced stove. Dry sample (0.5 g) was added with 6.0 ml of HNO3 (65%), 1.0 ml of H2O2 (30%), and 0.5 ml of HF. The solutions were filtered and stored in PET bottles and deionized water was added until the solution equaled 25 ml, while all of the others were stored in PET bottles with deionized water added until the solution equaled 50 ml. All of the other samples (milk, dairy products, tissues) were dried at 70 1C in a forced stove until of dry weight. Dry sample (0.3 g), finely crushed, was added with 6.0 ml of HNO3 (65%) and 1.0 ml of H2O2 (30%). The determination of Hg content in dry samples was carried out using automatic mercury analyzer AMA 254, which is able to measure Hg0 concentrations
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without a chemical pre-treatment. The instrument detection limit was 0.01 ng. From this point forward, all metal concentrations are expressed as micrograms/gram dry weight.
3. Data analysis At first, we carried out an explorative statistical analysis of the concentration values measured in the different kinds of samples and we compared our data with other measures presented in the literature. The explorative parameters are N, sample number; m, mean value; r, range; SD, standard deviation; and VC%, percentage variation coefficient. In order to point out some variations in metal patterns observed in the different sampling periods, for each metal we compare summer mean values and winter mean values. In particular, we apply a small-sample pooled two-tailed t test with a significance level of 5% to highlight the cases in which the mean values are significantly different (Weiss and Hassett, 1987). In order to study the relationships among the concentrations of the same metal in different samples, we individuate the following hypothetical chains: fodder–milk–ricotta, fodder–milk–cheese– mature cheese, fodder–target organ; along each chain we calculate the percentage persistence coefficient, which is defined as cp ¼
C i;j C fodder;j
%:
Ci,j represents the concentration of the jth metal in the ith sample of the chain. At the end, we apply a multivariate tool to investigate the correlation structure of our data (Legendre and Legendre, 1985). In particular, the analysis aims to characterize the spatial patterns of the metals and to evaluate the influence of the external variables characterizing the farms. We analyze three [farms metals] data matrices: the fodder data matrix, MFd [9 5], the milk data matrix MMk [12 5], the ricotta data matrix MRc [11 5]. For each matrix, we calculate the farm association matrices using the Euclidean distance: AFd [9 9], AMk [12 12], ARc [11 11]. In order to individuate the homogenous subgroups of farms, we apply the clustering algorithm at complete linkage. Each cluster may be characterized by means of endogenous indices (centroids are the mean values of metal concentrations measured in samples collected in the farms included in the cluster) and by means of exogenous indices (mean values of variables external to the clustering procedure). The correspondence among endogenous and exogenous indices allows us to explain the clustering structure.
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4. Results and discussion Tables 3–6 show a summary of the explorative analysis and a comparison between our results and the concentrations measured in similar samples from different sites. We note that in fodder samples (Table 3) we measured Cd, Pb, and Hg concentrations comparable to values observed in other sites and that we recorded Cr and Mn levels higher than values reported in the literature. For milk and diary product samples (Tables 4 and 5), we observed high Cr and Pb concentrations, while Cd and Mn showed the same order of magnitude as the values observed in other sites. Regarding the tissue analysis (Table 6), we note that Cd concentrations measured in kidney samples collected in the AV4, SA1, NA1, and CE1 farms were comparable to values observed in samples coming from a polluted site, while the levels measured in liver and in muscle samples were comparable to those observed in a control site. In the explorative analysis, the percentage variation coefficient (VC% ), defined as the percentage ratio between the standard deviation and the mean value, gives us useful information for discussing the metal concentrations measured in the different samples. For the fodder samples, we note that all of the metals showed high values for the variation coefficient (VC489%), highlighting great spatial variability. In fact, we measured the highest values of metal concentrations in the site nearest to a metropolitan area (farm NA1), while the lowest values were observed in the sample from farm CE1, a site characterized by the lowest anthropogenic pressure index. Heavy metal concentrations in milk samples showed a spatial variability lower than the heavy metal concentrations in fodder samples. In this case, for all of the metals, VC% values were lower than 35%. Only for Cr did we observe a VC equal to 65%. In dairy products, the VC% values were higher than the VC% values observed in the milk samples and were lower than the VC% values observed in the fodder samples (for ricotta samples they were in the range of 48–73%, for cheese samples they were in the range of 50–93%, and for mature cheese samples they were in the range of 34–57%). Only for chromium concentrations in cheese samples did we observe low VC% values (for cheese, the VC was 16%, and for mature cheese the VC was 22%). Results show us that the role of the ovine in the milk and dairy products reduces the variability of heavy metal concentrations, with respect to the fodder samples, suggesting a common behavior in metal metabolism. Moreover, the increase in spatial spread observed in dairy products, with respect to milk, may be ascribed both to different production processes and to different environmental conditions.
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Table 3 Results of explorative statistical analysis and data reported in the literature for heavy metal levels in fodder samples Element in mg/g dry wt Cd
Cr
SD VC%
0.52 0.07 1.85 0.63 121
6.5 0.3 35.6 10.5 162
m m
0.57 o0.10
N m r
Fodder
Data reported in the literature Feedinga Grass silage (cattle feed)b
0.12 0.21
Pb
Mn
Hg
1.2 0.3 4.1 1.1 90
101 13 256 89 89
10 0.0056 0.0007 0.0207 0.0059 105
0.70 o1
11.7 o0.10
N, sample number; m, mean value; r, range; SD, standard deviation; VC%, percentage variation coefficient. a Coni et al. (1996). b Nicholson et al. (1999).
Table 4 Results of explorative statistical analysis and data reported in the literature for heavy metal levels in milk samples Elements in mg/g dry wt
Milk
N m r SD VC%
Data reported in the literature Raw milk (sheep)a Raw milk (goat)a
m m
Cd
Cr
Pb
Mn
Hg
0.06 0.04 0.11 0.02 35
0.20 0.06 0.48 0.13 65
0.20 0.09 0.28 0.06 32
0.13 0.07 0.19 0.03 23
13 0.0025 0.0012 0.0037 0.0008 33
0.15 0.15
0.02 0.03
0.09 0.05
0.31 0.13
N, sample number; m, mean value; r, range; SD, standard deviation; VC%, percentage variation coefficient. a Coni et al. (1996).
Regarding the VC% values observed in the tissues, we noted high values for all of the metals in fat samples (VC% values were higher than 55%). For liver, muscle, and kidney samples different metals had different behaviors: Pb and Mn concentrations showed a small spread (VC% values lower than 20%) and Cd and Hg values ranged throughout a larger interval (VC% values of about 48%). In order to investigate the variability of samples collected in different seasons, we compared mean values of metal concentrations measured in summer samples and mean values of metal concentrations measured in winter samples (Table 7). The results of a small-sample pooled two-tailed t test showed that in only four cases were the summer mean values different from the winter mean values. Particularly for milk samples, we had significant differences in cadmium levels (Cdsummer ¼ 0.0570.01, Cdwinter ¼ 0.0770.02 mg/g dry wt) and in lead levels (Pbsummer ¼ 0.1770.06, Pbwinter ¼ 0.227
0.06 mg/g dry wt). For ricotta samples, we had significant differences in cadmium levels (Cdsummer ¼ 0.0870.03, Cdwinter ¼ 0.1270.07 mg/g dry wt) and in chromium levels (Crsummer ¼ 0.1370.09, Crwinter ¼ 0.3370.20 mg/g dry wt). These variations may be ascribable to changes in ovine feeding during the year. In all of the other examined cases (84%), the observed differences from summer to winter were not statistically significant. Consequently, we suppose that the seasonality did not have a remarkable influence on our data. In order to investigate the relationships among metal concentrations measured in different kinds of samples, we discuss the percentage persistence coefficient values. We investigate the fodder–milk–ricotta chain (Fig. 2A), the fodder–milk–cheese–mature cheese chain (Fig. 2B). and the fodder–target organ chains (Fig. 3). For the first chain, we note that Cd, Cr, Mn, and Pb showed a common behavior: cp values for ricotta samples increased in comparison with the values
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Table 5 Results of explorative statistical analysis and data reported in the literature for heavy metal levels in dairy product samples Element in mg/g dry wt
Ricotta
N m r SD VC%
Cheese
N m r SD VC%
Mature cheese
N m r SD VC%
Data reported in the literature Cheese after ripening for 1 week (sheep)a Cheese after ripening for 1 week (goat)a Commercial cheese (sheep)a Commercial cheese (goat)a
m m m m
Cd
Cr
Pb
Mn
Hg
0.10 0.06 0.27 0.06 56
0.27 0.03 0.71 0.19 73
0.43 0.12 0.85 0.22 52
0.22 0.06 0.40 0.11 48
12 0.0011 0.0006 0.0028 0.0006 55
0.11 0.04 0.40 0.11 93
0.42 0.34 0.53 0.07 16
0.35 0.11 1.15 0.31 89
0.36 0.17 0.80 0.18 50
10 0.0065 0.0010 0.0184 0.0062 96
0.11 0.06 0.15 0.04 34
0.47 0.34 0.67 0.10 22
0.58 0.21 1.07 0.27 46
0.45 0.22 1.10 0.29 66
8 0.0141 0.0020 0.0561 0.0169 57
0.15 0.14 0.12 0.11
0.02 0.05 0.04 0.06
0.08 0.05 0.10 0.08
0.54 0.22 0.42 0.19
N, sample number; m, mean value; r, range; SD, standard deviation; VC%, percentage variation coefficient. a Coni et al. (1996).
calculated for milk samples. This behavior was more evident for Pb (cpmilk ¼ 17% and cpricotta ¼ 42%), while for Cd, Cr, and Mn the observed differences between cpmilk and cpricotta were lower than 10%. We highlight that for Mn, the persistence coefficient values were very small (cpmilk ¼ 0.2% and cpricotta ¼ 0.3%), pointing out a weak link between Mn concentration in fodder samples and Mn concentrations in milk and ricotta samples. Hg showed a different behavior; for this metal the decrements of coefficient values along the chain were very high (cpmilk ¼ 52% and cpricotta ¼ 23%). This is probably due both to the high volatility of Hg and to the production process of ricotta. For the second chain (Fig. 2B), we note that for all of the metals cpmilkocpcheese. The difference was particularly relevant for Hg (40%) and Pb (16%). This result is in agreement with chemical–physical mechanisms linked to the curdling and salting steps in the cheese production process (Coni et al., 1996) In the successive step of the chain (the maturation process), the increases were much less evident, except for Pb, (cpmaturecheesecpcheese ¼ 14%). This last is probably due to the influence of environmental pollution. For the fodder–target organ chains (Fig. 3), we observed that the persistence coefficient values for Mn
were very low. For all of the other metals, we found the highest values of this coefficient in kidney samples. In particular, for Cd we note that cpkidney4cpfodder, in agreement with the Cd concentration factor reported by Sedki et al. (2003). The results of the clustering procedure, aimed to investigate the metal spatial patterns, are summarized in Table 8 (centroid tables) and in Fig. 4 (dendrograms). For the fodder samples, we point out two homogeneous subgroups of farms: KR4, KR2, NA1 and CE2, SA1, AV2, AV1, CE1, KR1. The average value of the anthropogenic pressure index (Pave) for each cluster was Pave ¼ 4.0 and 2.1, respectively. Moreover, the first cluster was characterized by higher metal concentrations (Table 8). These statistical results suggest that the clusters are based on different anthropogenic pressure levels due to industrial and anthropogenic emissions. For milk samples, the clustering procedure highlights two groups of farms, AV1, AV3, SA1, KR4 and AV2, KR1, KR2, NA1, KR3, CE1, CE2 and an isolated element AV4. In this case, the values were similar for the two clusters, while for AV4 the Pave was lower. Additionally, the centroids of the two clusters were comparable, except for Cr concentrations. They were 0.08 and 0.27 mg/g dry weight for the two clusters,
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Table 6 Results of explorative statistical analysis and data reported in the literature for heavy metal levels in tissue samples
Table 7 Mean values of concentrations measured in summer and in winter Element in mg/g dry wt
Element in mg/g dry weight
Liver
N m r SD VC%
Muscle
N m r SD VC%
Kidney
N m r SD VC%
Fat
N m r SD VC%
Pb
Mn
Hg
0.33 0.16 0.62 0.25 77
1.5 1.4 1.6 0.1 7
15.3 12.9 19.2 3.4 22
3 0.0051 0.0032 0.0084 0.0029 57
0.16 0.11 0.19 0.03 20
1.6 1.3 2.2 0.4 25
1.3 1.2 1.4 0.1 5
4 0.0036 0.0015 0.0066 0.0022 60
6.71 4.24 10.87 2.88 43
2.0 1.5 2.2 0.3 16
7.0 5.4 9.1 1.7 25
4 0.0085 0.0053 0.0103 0.0023 27
0.31 0.04 0.79 0.34 112
0.31 0.07 0.74 0.30 99
0.7 0.3 1.2 0.4 57
4 0.0075 0.0017 0.0177 0.0073 98
Data reported in the literature Bovine samples from a polluted sitea Liver m 5.1 Muscle m 0.6 Kidney m 10.3 Bovine samples from a control sitea Liver m 1.7 Muscle m 0.2 Kidney m 2.2 N, sample number; m, mean value; r, range; SD, standard deviation; VC%, percentage variation coefficient. a Sedki et al. (2003).
Cr
Pb
Mn
Hg
Summer (n ¼ 3) Winter (n ¼ 7)
0.89 (0.83) 0.36 (0.51)
6 (2.7) 7 (13)
1.1 (0.3) 1.3 (1.4)
65 (33) 116 (103)
0.007 (0.002) 0.005 (0.007)
Milk
Summer (n ¼ 5) Winter (n ¼ 8)
0.05 (0.01) 0.07 (0.02)
0.15 (0.18) 0.23 (0.08)
0.17 (0.06) 0.22 (0.06)
0.11 (0.03) 0.13 (0.03)
0.002 (0.001) 0.003 (0.001)
Ricotta
Summer (n ¼ 4) Winter (n ¼ 8)
0.08 (0.03) 0.12 (0.07)
0.13 (0.09) 0.33 (0.2)
0.36 (0.25) 0.47 (0.22)
0.19 (0.1) 0.23 (0.11)
0.001 (0.001) 0.001 (0.001)
Cheese
Summer (n ¼ 2) Winter (n ¼ 8)
0.08 (0.01) 0.12 (0.12)
0.38 (0.05) 0.43 (0.07)
0.77 (0.55) 0.24 (0.14)
0.26 (0.02) 0.39 (0.19)
0.002 (0.001) 0.008 (0.007)
Mature cheese
Summer (n ¼ 2) Winter (n ¼ 6)
0.12 (0.02) 0.1 (0.04)
0.42 (0.11) 0.48 (0.11)
0.81 (0.37) 0.5 (0.21)
0.67 (0.62) 0.37 (0.14)
0.03 (0.04) 0.01 (0.007)
For each value, the standard deviation is shown in parentheses. The cases in which the means are significantly different are reported in bold.
100 80 60 40 20 0 Fodder
(A)
Milk
Cd
Cr
Mn
Ricotta Pb
Hg
100 80
cp(%)
respectively, and 0.48 mg/g dry weight for the isolated element (farm AV4). Thus, the statistical analysis of milk samples allows us to classify the farms on the basis of Cr levels alone. For ricotta samples, we obtained two clusters, AV3, KR1, KR2, KR3, KR4, CE2 and AV1, AV2, NA1, CE1. The Pave values for these were 3.6 and 1.0, respectively. In this case the multivariate procedure divided the farms on the basis of Cr and Pb levels, but we observed higher heavy metal concentrations in the farms for which the average value of anthropogenic pressure index was lower. The index G, instead, is not an exogenous variable characterizing the clusters; in fact, in all of the clusters there was no specific dominance of sites with similar microclimatic features.
Cd Fodder
cp(%)
Cd
60 40 20 0
(B)
Fodder
Milk Cd
Cr
Cheese Mn
Pb
Mature chese Hg
Fig. 2. Percentage persistence coefficient values for the fodder–milk–ricotta chain (A) and for the fodder–milk–cheese–mature cheese chain (B).
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5. Conclusions This paper shows the analysis of relationships among five heavy metal concentrations measured in a variety of samples (ovine food, milk, dairy products, and tissues) collected in ovine farms of Southern Italy. The analyzed samples are representative of different links in the food chain. Our approach allows us both to investigate the distribution of the metals along this food chain, taking into account different spatial and temporal patterns, and to evaluate the influence of anthropogenic emissions in the surrounding areas. The study is based on the application of different statistical methods (univariate, bivariate, and multivariate techniques) and on the identification of some indicators (variation coefficient, persistence coefficient, cluster centroids) able to explain the results. The analysis of the variation coefficient allows us to highlight that the variability of heavy metal concentrations in milk and in dairy products is lower than in fodder samples. This result is ascribable to the influence of ovine metal metabolism. Moreover, the increase of
120 Leaver
100
Muscle Kidney
cp(%)
80
Fat 60 40 20 0 Cd
Mn
Pb
Hg
Fig. 3. Percentage persistence coefficient values for the fodder–target organ chains.
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spatial spread observed in dairy products, with respect to milk, may be related both to different production processes and to different environmental conditions. Regarding the temporal patterns, the seasonal analysis did not point out significant differences in the contamination levels among samples collected during different periods (winter or summer). The analysis of the persistence coefficient highlights the relationships among the metals along the different chain links. Particularly, we note that Mn shows, in all of the examined cases, the lowest values of persistence, while the highest values are shown by Hg. The concentration variations from milk to products depend both on the kind of diary product (cheese or ricotta) and on production processes. Regarding organs and tissues analysis, although the number of examined samples is low, the data showed Cd, Pb, and Hg to have concentrations on the same magnitude of order in both tissues and in fodder. This is particularly true for liver and kidney; they show high values for the persistence coefficient, confirming that these target organs play a specific role as metal bioaccumulators. A comparison among the values of the persistence coefficient observed in all of the examined chains highlights that in the milk and dairy product chains the influence of feed contamination is less evident than in the tissue chains. Regarding the multivariate analysis, the clustering on the metal concentrations measured in the fodder samples suggests that the farm classification is mainly determined by different anthropogenic pressure levels due to industrial and anthropogenic emissions, in accord with the analysis based on the VC% values. On the contrary, in the case of the milk samples, the farm subgroups have similar Pave values and similar centroids, except for the Cr values. Chromium assumes the role of the discriminant element among the samples. This result, combined with the discussed VC% patterns, seems to indicate that the characteristics of milk samples
Table 8 Centroid table Element in mg/g dry wt Cd
Cr
Pb
Mn
Hg
Pave
Fodder samples Cluster1: KR2, KR4, NA1 Cluster2: AV12, AV2, KR1, SA1, CE1, CE2
0.60 0.52
14.4 2.8
1.8 0.9
218 53
0.0097 0.0037
4.0 1.8
Milk samples Cluster1: AV1, AV3, SA1, KR4 Cluster2: AV2, KR1, KR2, NA1, KR3, CE1, CE2 AV4
0.052 0.062 0.070
0.08 0.27 0.48
0.19 0.19 0.22
0.12 0.12 0.13
0.0027 0.0023 0.0026
3.0 2.1 1.0
Ricotta samples Cluster1: AV3, KR1, KR2, SA1, KR3, KR4, CE2 Cluster2: AV1, AV2, NA1, CE1
0.09 0.12
0.21 0.34
0.33 0.61
0.22 0.22
0.0010 0.0014
3.6 1.0
The mean values of the P index (Pave) are shown in the last column.
ARTICLE IN PRESS 56
R. Caggiano et al. / Environmental Research 99 (2005) 48–57
Fig. 4. Results of the clustering procedure. For each data matrix (fodder, milk, and ricotta), the clusters are represented in grey.
are determined mainly by ovine features and that the external conditions have a minor role. For ricotta samples, we obtain a farm classification that reflects both of the aspects. In this case the multivariate procedure divides the samples on the basis of Cr and Pb levels, obtaining subgroups with different Pave values, but the higher heavy metal concentrations correspond to the lower values of the anthropogenic pressure index. This result confirms the low dominance of external sources of contamination on metal levels in
milk and dairy products. In the end, we found that the independence of metal levels from site microclimatic features was verified by all of our samples.
Acknowledgments We thank Prof. G. Paino for helpful discussions. Furthermore, we thank the ASLs and the farms that have kindly cooperated with us.
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References Albers, B.P., Steindl, H., Schimmack, W., Bunzl, K., 2000. Soil-toplant and plant-to-cow’s milk transfer of radiocaesium in alpine pastures: significance of seasonal variability. Chemosphere 41, 717–723. Alberti-Fidanza, A., Burini, G., Perriello, G., Fidanza, F., 2003. Trace elements intake and status of Italian subjects living in the Gubbio area. Environ. Res. 91, 71–77. Bratakos, M.S., Lazos, E.S., Bratakos, S.M., 2002. Chromium content of selected Greek foods. Sci. Total Environ. 290, 47–58. Burger, J., Gaines, K.F., Boring, C.S., Stephens, W.L., Snodgrass, J., Dixon, C., McMahon, M., Shukla, S., Shukla, T., Gochfeld, M., 2002. Metal levels in fish from the Savannah River: potential hazards to fish and other receptors. Environ. Res. A 89, 85–97. Caggiano, R., Macchiato, M.F., Ragosta, M., 1998. Background level characterization of heavy metals soil concentrations in an industrial area of Southern Italy. Nuovo. Cimento. C 21, 49–63. Caggiano, R., D’Emilio, M., Macchiato, M.F., Ragosta, M., 2004. Heavy metals in ryegrass species versus metal concentrations in atmospheric particulate in an industrial area of Southern Italy. Environ. Monit. Assess., in press (prepublication date 20 April 2004). Chen, H., Lee, C., Liao, P., Guo, Y., Chen, C., Su, H., 2003. Associations between dietary intake and serum polychlorinated dibenzo-p-dioxin and dibenzofuran (PCDD/F) levels in Taiwanese. Environ. Res. 91, 172–178. Coni, E., Bocca, A., Coppolelli, P., Caroli, S., Cavallucci, C., Trabalza Marinucci, M., 1996. Minor and trace element content in sheep and goat milk and dairy products. Food Chem. 57, 253–260. Coni, E., Bocca, A., Caroli, S., 1999. Minor and trace element content of two typical Italian sheep dairy products. J. Dairy Res. 66, 589–598. Cuny, D., Van Haluwyn, C., Pesch, R., 2001. Biomonitoring of trace elements in air and soil compartments along the major motorway in France. Water Air Soil Pollut. 125, 273–289. de Souza-Lima, R.G., Araujo, F.G., Maia, M.F., de Silveira Braz Pinto, A.S., 2002. Evaluation of heavy metals in fish of the Sepetiba and Ilha Grande Bays, Rio de Janeiro, Brazil. Environ. Res. 89, 171–179. Koc- ak, M., Nimmo, M., Kubilay, N., Herut, B., 2004. Spatiotemporal aerosol trace metal concentrations and sources in the Levantine Basin of the Eastern Mediterranean. Atmos. Environ. 38, 2133–2144. Legendre, L., Legendre, P., 1985. Numerical Ecology. Elsevier, Amsterdam.
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Licata, P., Trombetta, D., Cristiani, M., Giofre`, F., Martino, D., Calo`, M., Naccari, F., 2004. Levels of toxic and essential metals in samples of bovine milk from various dairy farms in Calabria, Italy. Environ. Res. 30, 1–6. Lopez-Alonso, M., Benedito, J.L., Miranda, M., Castillo, C., Hernandez, J., Shore, R.F., 2000. Arsenic, cadmium, lead, copper and zinc in cattle from Galicia, NW Spain. Sci. Total Environ. 246, 237–248. Loska, K., Wiechula, D., Korus, I., 2004. Metal contamination of farming soils affected by industry. Environ. Int. 30, 159–165. Macchiato, M., La Rotonda, L., Lapenna, V., Ragosta, M., 1995. Time modelling and spatial clustering of daily ambient temperature: an application in Southern Italy. Environmetrics 6, 31–53. Merdivan, M., Yilmaz, E., Hamamci, C., Aygun, R.S., 2004. Basic nutrients and element contents of white cheese of Diyarbakir in Turkey. Food Chem. 87, 163–171. Nardone, A., Valfre`, F., 1999. Effects of changing production methods on quality of meat, milk and eggs. Livestock Prod. Sci. 59, 165–182. Nicholson, F.A., Chambers, B.J., Williams, J.R., Unwin, R.J., 1999. Heavy metal contents of livestock feeds and animal manures in England and Wales. Biores. Tech. 70, 23–31. Peyrovan, H., Amirabadi, A., 1996. Heterogeneity measurement of trace elements distribution in sheep liver using the PIXE method. Nucl. Instrum. Methods B 109/110, 309–311. Prankel, S.H., Nixon, R.M., Phillips, C.J.C., 2004. Meta-analysis of feeding trials investigating cadmium accumulation in the livers and kidneys of sheep. Environ. Res. 94, 171–183. Ragosta, M., Caggiano, R., D’ Emilio, M., Macchiato, M., 2002. Source origin and parameters influencing levels of heavy metals in TSP, in an industrial background area of Southern Italy. Atmos. Environ. 36, 3071–3087. Rajaratnam, G., Winder, C., An, M., 2002. Metals in drinking water from new housing estates in Sydney area. Environ. Res. 89, 165–170. Sedki, A., Lekouch, N., Gamon, S., Pineau, A., 2003. Toxic and essential trace metals in muscle, liver and kidney of bovine from a polluted area of Morocco. Sci. Total Environ. 317, 201–205. Sivertsen, T., Daae, H.L., Godal, A., Sand, G., 1995. Ruminant uptake of nickel and other elements from industrial air pollution in the Norwegian–Russian border area. Environ. Pollut. 90, 75–81. Toso, B., Procida, G., Stefanon, B., 2002. Determination of volatile compounds in cows’ milk using headspace GC–MS. J. Dairy Res. 69, 569–577. Weiss, N.A., Hassett, M.J., 1987. Introductory Statistics. AddisonWesley, Reading, MA, USA.
Environmental Research 99 (2005) 48–57 www.elsevier.com/locate/envres
Metal levels in fodder, milk, dairy products, and tissues sampled in ovine farms of Southern Italy$ Rosa Caggianoa, Serena Sabiaa, Mariagrazia D’Emiliob, Maria Macchiatob, Aniello Anastasioc, Maria Ragostad,, Salvatore Painoe a
IMAA, Istituto di Metodologie per l’ Analisi Ambientale CNR, Tito Scalo (PZ), Italy b INFM, Dipartimento di Scienze Fisiche, Universita` Federico II, Napoli, Italy c Dipartimento di Scienze Zootecniche e Ispezione degli Alimenti, Universita` Federico II, Napoli, Italy d INFM, Dipartimento di Ingegneria e Fisica dell’ Ambiente, Universita` della Basilicata, Via dell’Ateneo Lucano, 85100 Potenza, Italy e Dipartimento di Scienze della Produzione Animale, Universita` della Basilicata, Potenza, Italy Received 28 June 2004; received in revised form 29 October 2004; accepted 8 November 2004 Available online 25 December 2004
Abstract We measured Cd, Cr, Hg, Mn, and Pb levels in samples of fodder, milk, dairy products, and tissues collected from 12 ovine farms in the regions of Campania and Calabria (Southern Italy). The areas in which the farms are located show different levels of anthropogenic pressure. The main purpose of this study is the identification and the analysis of relationships among metal concentrations observed in samples representative of different links in the food chain. Particularly, we apply univariate, bivariate, and multivariate statistical techniques to identify the correlation structure of our data set and to evaluate the influence of anthropogenic activity. We discuss the results, focusing the analysis on the spatial and the temporal patterns of metal concentrations. r 2004 Elsevier Inc. All rights reserved. Keywords: Heavy metals; Ovine; Milk; Dairy products; Tissues
1. Introduction In the higher organisms, the intake of trace elements occurs mainly through the respiratory system or through the food chain. Particularly, many dangerous elements or compounds, such as dioxins, pesticides, metals, and metalloids, accumulate along the food chain. In this way, the food chain becomes the main gateway for persistent toxicants to enter higher organisms. Furthermore, these compounds generally have an anthropogenic origin, and thus their concentrations in $ This work was supported by grants from INFM (Istituto Nazionale per la Fisica della Materia) Biophysics Section funds. Any studies involving experimental animals were conducted in accordance with national and institutional guidelines for the protection of animal welfare. Corresponding author. Fax: +39 971 205169. E-mail address: [email protected] (M. Ragosta).
0013-9351/$ - see front matter r 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.envres.2004.11.002
the environment grow with the increase of urban, agricultural, and industrial emissions. In this framework, the control of the levels of these elements in food is not only an important aspect of food quality but also an indirect monitoring system of anthropogenic activity impacts on soil, water, and air (Albers et al., 2000; Rajaratnam et al., 2002; Burger et al., 2002; Bratakos et al., 2002; Toso et al., 2002; Alberti-Fidanza et al., 2003; Chen et al., 2003; Prankel et al., 2004). We focus this study on heavy metals. Their concentrations (bioavailable or total content) in the environment are generally used as an early indicator of contamination phenomena both in programs of soil quality control and in air quality monitoring (Caggiano et al., 1998; Ragosta et al., 2002; Koc- ak et al., 2004; Loska et al., 2004). Particularly, direct correlations between the concentrations of these elements in air or in soil and the increase of anthropogenic emissions have
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been observed, not only on large spatial–temporal scales, but also over short periods and in small areas. The heavy metal levels are examined also in biomonitoring procedures that are based on vegetal accumulators and that evaluate environmental stress in urban and industrial areas (Cuny et al., 2001; Caggiano et al., 2004). We measured Cd, Cr, Hg, Mn, and Pb levels in samples of fodder, milk, dairy products, and tissues from ovine farms. The study was carried out in 12 farms located in six zones of the regions of Campania and Calabria (Southern Italy) that are characterized by different levels of anthropogenic activity impact. In the literature, we find studies dealing with heavy metal concentrations in milk, in dairy products, or in tissues (Sivertsen et al., 1995; Peyrovan and Amirabadi, 1996; Lopez-Alonso et al., 2000; de Souza-Lima et al., 2002; Sedki et al., 2003; Licata et al., 2004; Merdivan et al., 2004), but only in a few cases (Coni et al., 1996, 1999) has a set of metals been measured in different kinds of samples collected in farms spread over a large area. Furthermore, these studies highlight that it is difficult to individuate well-defined relationships among heavy metal concentrations in animal feed, milk, diary products, and tissues. In fact, animal nutrition affects the hygienic quality of milk, but it is not easy to control and to evaluate contamination coming from industrial processes, agricultural practices, and atmospheric depositions. Moreover, some studies put in evidence that the manufacturing processes may influence heavy metal concentrations in diary products (Coni et al., 1996; Nardone and Valfre`, 1999). In this context, the main purpose of this work is the identification and analysis of the relationships among the metal concentrations observed in different kinds of samples representative of different links in the food chain. Particularly, we analyze the spatial and temporal patterns of metal concentrations in order to point out a relationship among metal levels observed in ovine food and metal levels observed in milk and in dairy products. Analogously, we analyze the relationships among metal concentrations measured in fodder samples and metal concentrations measured in different tissues (liver, muscle, kidney, and fat) commonly considered target organs (Sedki et al., 2003). Finally, we investigate also the spatial distribution of metals measured in different samples, taking into account the anthropogenic pressure in the areas surrounding the farms.
49
Fig. 1. Study area. The farms are located in the regions of Campania and Calabria.
regions of Southern Italy. The map of the sampling sites is shown in Fig. 1. We describe the geographical features and the anthropogenic characteristics of the sampling sites using two aggregated indices (Table 1), which are defined as follows: Index G takes into account the altitude (Q) and the distance from the sea (D), which well identify the microclimatic features of the area (Macchiato et al., 1995). In particular, GM indicates the mountainous inner sites [Q values more of than 700 m above sea level (a.s.l.) and D values of more than 30 km], GH the hill sites (Q values in the range of 300–600 m a.s.l. and D values in the range of 10–25 km), and GC the coastal sites (Q values of lower than 300 m a.s.l. and D values of lower than 10 km). Index P is a cumulative index that takes into account the presence of different emission sources (high volume of traffic, industrial plants, dumps, waste incinerators, metropolitan areas, small-medium towns, villages, intensive agricultural activities); it ranges from 0 (for zones with the lowest anthropogenic pressure) to 7 (for the zones with the highest anthropogenic pressure). 2.2. Sampling protocols
2. Experimental data and methods 2.1. Sampling sites All of the samples were collected during Summer 2001 and Winter 2002 in 12 ovine farms located in two
We analyzed samples of fodder, milk, ricotta, cheese (8 days of maturation), mature cheese (60 days of maturation), liver, muscle, kidney, and fat. The samples were collected during two different periods: July 2001 (summer samples are labeled s01) and January–March
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50 Table 1 Parameters for farm characterization Site label
AV1 AV2 AV3 AV4 SA1 KR1 KR2 NA1 KR3 KR4 CE1 CE2
Region
G index
Campania Campania Campania Campania Campania Calabria Calabria Campania Calabria Calabria Campania Campania
P index
GM GM GM GM GC GC GC GH GC GC GH GH
T
InP
D
Inc
sUS
mUS
lUS
Sum
0 0 0 0 1 1 1 1 1 1 0 0
0 0 1 0 1 1 0 1 0 0 0 0
0 0 0 0 1 1 1 0 0 0 0 1
0 0 1 0 0 0 0 0 0 0 0 0
0 0 1 1 1 1 1 1 1 1 0 0
0 0 0 0 1 1 1 1 1 1 0 0
0 0 0 0 0 0 0 1 0 0 0 0
0 0 3 1 5 5 4 5 3 3 0 1
T, traffic; InP, industrial plants; D, dumps; Inc, incinerators; sUS, small urban sites; mUS, medium urban sites; lUS, large urban sites; GM, mountainous inner sites; GH, hill sites; GC, coastal sites; Sum ¼ P index value. Table 2 Available samples for each sampling site Site label
AV1 AV2 AV3 AV4 SA1 KR1 KR2 NA1 AV2 KR3 KR4 CE1 CE2
Sampling label
s01 s01 s01 s01 s01 w02 w02 w02 w02 w02 w02 w02 w02 N
Available samples Fodder
Milk
Ricotta
Cheese
Mature cheese
’ ’ ’ ’ ’ ’ ’ ’ 13
’ ’ ’ ’ ’ ’ ’ ’ 12
’ ’ ’ ’ ’ ’ ’ ’ 10
’
’ ’ ’ ’ ’ ’ ’ 10
’ ’ ’ ’ ’ 8
Liver
Muscle
Kidney
Fat
’
’
’
’
’
’
’
’
3
4
4
4
, summer; ’, winter; s01, summer 2001; w02, winter 2002; N, sample number. 2002 (winter samples are labeled w02). The sampling plan is shown in Table 2. All of the samples were collected according to Italian norms and following rigorous procedures in order to minimize possible external contaminations. In particular, each milk sample comprised seven takings of milk during the morning milking. In eight of the 12 farms, the milk samples were divided in three parts; the first was analyzed as milk, the second was used to produce ricotta samples, and the third was used to produce cheese and mature cheese samples. 2.3. Trace elements analysis The elemental analysis of Cd, Cr, Mn, and Pb was performed using a Varian AA200 atomic absorption spectrophotometer equipped with a graphite furnace.
A microwave system was used for the acid digestion of all of the samples. The fodder samples were dried at 70 1C for 48 h in a forced stove. Dry sample (0.5 g) was added with 6.0 ml of HNO3 (65%), 1.0 ml of H2O2 (30%), and 0.5 ml of HF. The solutions were filtered and stored in PET bottles and deionized water was added until the solution equaled 25 ml, while all of the others were stored in PET bottles with deionized water added until the solution equaled 50 ml. All of the other samples (milk, dairy products, tissues) were dried at 70 1C in a forced stove until of dry weight. Dry sample (0.3 g), finely crushed, was added with 6.0 ml of HNO3 (65%) and 1.0 ml of H2O2 (30%). The determination of Hg content in dry samples was carried out using automatic mercury analyzer AMA 254, which is able to measure Hg0 concentrations
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without a chemical pre-treatment. The instrument detection limit was 0.01 ng. From this point forward, all metal concentrations are expressed as micrograms/gram dry weight.
3. Data analysis At first, we carried out an explorative statistical analysis of the concentration values measured in the different kinds of samples and we compared our data with other measures presented in the literature. The explorative parameters are N, sample number; m, mean value; r, range; SD, standard deviation; and VC%, percentage variation coefficient. In order to point out some variations in metal patterns observed in the different sampling periods, for each metal we compare summer mean values and winter mean values. In particular, we apply a small-sample pooled two-tailed t test with a significance level of 5% to highlight the cases in which the mean values are significantly different (Weiss and Hassett, 1987). In order to study the relationships among the concentrations of the same metal in different samples, we individuate the following hypothetical chains: fodder–milk–ricotta, fodder–milk–cheese– mature cheese, fodder–target organ; along each chain we calculate the percentage persistence coefficient, which is defined as cp ¼
C i;j C fodder;j
%:
Ci,j represents the concentration of the jth metal in the ith sample of the chain. At the end, we apply a multivariate tool to investigate the correlation structure of our data (Legendre and Legendre, 1985). In particular, the analysis aims to characterize the spatial patterns of the metals and to evaluate the influence of the external variables characterizing the farms. We analyze three [farms metals] data matrices: the fodder data matrix, MFd [9 5], the milk data matrix MMk [12 5], the ricotta data matrix MRc [11 5]. For each matrix, we calculate the farm association matrices using the Euclidean distance: AFd [9 9], AMk [12 12], ARc [11 11]. In order to individuate the homogenous subgroups of farms, we apply the clustering algorithm at complete linkage. Each cluster may be characterized by means of endogenous indices (centroids are the mean values of metal concentrations measured in samples collected in the farms included in the cluster) and by means of exogenous indices (mean values of variables external to the clustering procedure). The correspondence among endogenous and exogenous indices allows us to explain the clustering structure.
51
4. Results and discussion Tables 3–6 show a summary of the explorative analysis and a comparison between our results and the concentrations measured in similar samples from different sites. We note that in fodder samples (Table 3) we measured Cd, Pb, and Hg concentrations comparable to values observed in other sites and that we recorded Cr and Mn levels higher than values reported in the literature. For milk and diary product samples (Tables 4 and 5), we observed high Cr and Pb concentrations, while Cd and Mn showed the same order of magnitude as the values observed in other sites. Regarding the tissue analysis (Table 6), we note that Cd concentrations measured in kidney samples collected in the AV4, SA1, NA1, and CE1 farms were comparable to values observed in samples coming from a polluted site, while the levels measured in liver and in muscle samples were comparable to those observed in a control site. In the explorative analysis, the percentage variation coefficient (VC% ), defined as the percentage ratio between the standard deviation and the mean value, gives us useful information for discussing the metal concentrations measured in the different samples. For the fodder samples, we note that all of the metals showed high values for the variation coefficient (VC489%), highlighting great spatial variability. In fact, we measured the highest values of metal concentrations in the site nearest to a metropolitan area (farm NA1), while the lowest values were observed in the sample from farm CE1, a site characterized by the lowest anthropogenic pressure index. Heavy metal concentrations in milk samples showed a spatial variability lower than the heavy metal concentrations in fodder samples. In this case, for all of the metals, VC% values were lower than 35%. Only for Cr did we observe a VC equal to 65%. In dairy products, the VC% values were higher than the VC% values observed in the milk samples and were lower than the VC% values observed in the fodder samples (for ricotta samples they were in the range of 48–73%, for cheese samples they were in the range of 50–93%, and for mature cheese samples they were in the range of 34–57%). Only for chromium concentrations in cheese samples did we observe low VC% values (for cheese, the VC was 16%, and for mature cheese the VC was 22%). Results show us that the role of the ovine in the milk and dairy products reduces the variability of heavy metal concentrations, with respect to the fodder samples, suggesting a common behavior in metal metabolism. Moreover, the increase in spatial spread observed in dairy products, with respect to milk, may be ascribed both to different production processes and to different environmental conditions.
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Table 3 Results of explorative statistical analysis and data reported in the literature for heavy metal levels in fodder samples Element in mg/g dry wt Cd
Cr
SD VC%
0.52 0.07 1.85 0.63 121
6.5 0.3 35.6 10.5 162
m m
0.57 o0.10
N m r
Fodder
Data reported in the literature Feedinga Grass silage (cattle feed)b
0.12 0.21
Pb
Mn
Hg
1.2 0.3 4.1 1.1 90
101 13 256 89 89
10 0.0056 0.0007 0.0207 0.0059 105
0.70 o1
11.7 o0.10
N, sample number; m, mean value; r, range; SD, standard deviation; VC%, percentage variation coefficient. a Coni et al. (1996). b Nicholson et al. (1999).
Table 4 Results of explorative statistical analysis and data reported in the literature for heavy metal levels in milk samples Elements in mg/g dry wt
Milk
N m r SD VC%
Data reported in the literature Raw milk (sheep)a Raw milk (goat)a
m m
Cd
Cr
Pb
Mn
Hg
0.06 0.04 0.11 0.02 35
0.20 0.06 0.48 0.13 65
0.20 0.09 0.28 0.06 32
0.13 0.07 0.19 0.03 23
13 0.0025 0.0012 0.0037 0.0008 33
0.15 0.15
0.02 0.03
0.09 0.05
0.31 0.13
N, sample number; m, mean value; r, range; SD, standard deviation; VC%, percentage variation coefficient. a Coni et al. (1996).
Regarding the VC% values observed in the tissues, we noted high values for all of the metals in fat samples (VC% values were higher than 55%). For liver, muscle, and kidney samples different metals had different behaviors: Pb and Mn concentrations showed a small spread (VC% values lower than 20%) and Cd and Hg values ranged throughout a larger interval (VC% values of about 48%). In order to investigate the variability of samples collected in different seasons, we compared mean values of metal concentrations measured in summer samples and mean values of metal concentrations measured in winter samples (Table 7). The results of a small-sample pooled two-tailed t test showed that in only four cases were the summer mean values different from the winter mean values. Particularly for milk samples, we had significant differences in cadmium levels (Cdsummer ¼ 0.0570.01, Cdwinter ¼ 0.0770.02 mg/g dry wt) and in lead levels (Pbsummer ¼ 0.1770.06, Pbwinter ¼ 0.227
0.06 mg/g dry wt). For ricotta samples, we had significant differences in cadmium levels (Cdsummer ¼ 0.0870.03, Cdwinter ¼ 0.1270.07 mg/g dry wt) and in chromium levels (Crsummer ¼ 0.1370.09, Crwinter ¼ 0.3370.20 mg/g dry wt). These variations may be ascribable to changes in ovine feeding during the year. In all of the other examined cases (84%), the observed differences from summer to winter were not statistically significant. Consequently, we suppose that the seasonality did not have a remarkable influence on our data. In order to investigate the relationships among metal concentrations measured in different kinds of samples, we discuss the percentage persistence coefficient values. We investigate the fodder–milk–ricotta chain (Fig. 2A), the fodder–milk–cheese–mature cheese chain (Fig. 2B). and the fodder–target organ chains (Fig. 3). For the first chain, we note that Cd, Cr, Mn, and Pb showed a common behavior: cp values for ricotta samples increased in comparison with the values
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Table 5 Results of explorative statistical analysis and data reported in the literature for heavy metal levels in dairy product samples Element in mg/g dry wt
Ricotta
N m r SD VC%
Cheese
N m r SD VC%
Mature cheese
N m r SD VC%
Data reported in the literature Cheese after ripening for 1 week (sheep)a Cheese after ripening for 1 week (goat)a Commercial cheese (sheep)a Commercial cheese (goat)a
m m m m
Cd
Cr
Pb
Mn
Hg
0.10 0.06 0.27 0.06 56
0.27 0.03 0.71 0.19 73
0.43 0.12 0.85 0.22 52
0.22 0.06 0.40 0.11 48
12 0.0011 0.0006 0.0028 0.0006 55
0.11 0.04 0.40 0.11 93
0.42 0.34 0.53 0.07 16
0.35 0.11 1.15 0.31 89
0.36 0.17 0.80 0.18 50
10 0.0065 0.0010 0.0184 0.0062 96
0.11 0.06 0.15 0.04 34
0.47 0.34 0.67 0.10 22
0.58 0.21 1.07 0.27 46
0.45 0.22 1.10 0.29 66
8 0.0141 0.0020 0.0561 0.0169 57
0.15 0.14 0.12 0.11
0.02 0.05 0.04 0.06
0.08 0.05 0.10 0.08
0.54 0.22 0.42 0.19
N, sample number; m, mean value; r, range; SD, standard deviation; VC%, percentage variation coefficient. a Coni et al. (1996).
calculated for milk samples. This behavior was more evident for Pb (cpmilk ¼ 17% and cpricotta ¼ 42%), while for Cd, Cr, and Mn the observed differences between cpmilk and cpricotta were lower than 10%. We highlight that for Mn, the persistence coefficient values were very small (cpmilk ¼ 0.2% and cpricotta ¼ 0.3%), pointing out a weak link between Mn concentration in fodder samples and Mn concentrations in milk and ricotta samples. Hg showed a different behavior; for this metal the decrements of coefficient values along the chain were very high (cpmilk ¼ 52% and cpricotta ¼ 23%). This is probably due both to the high volatility of Hg and to the production process of ricotta. For the second chain (Fig. 2B), we note that for all of the metals cpmilkocpcheese. The difference was particularly relevant for Hg (40%) and Pb (16%). This result is in agreement with chemical–physical mechanisms linked to the curdling and salting steps in the cheese production process (Coni et al., 1996) In the successive step of the chain (the maturation process), the increases were much less evident, except for Pb, (cpmaturecheesecpcheese ¼ 14%). This last is probably due to the influence of environmental pollution. For the fodder–target organ chains (Fig. 3), we observed that the persistence coefficient values for Mn
were very low. For all of the other metals, we found the highest values of this coefficient in kidney samples. In particular, for Cd we note that cpkidney4cpfodder, in agreement with the Cd concentration factor reported by Sedki et al. (2003). The results of the clustering procedure, aimed to investigate the metal spatial patterns, are summarized in Table 8 (centroid tables) and in Fig. 4 (dendrograms). For the fodder samples, we point out two homogeneous subgroups of farms: KR4, KR2, NA1 and CE2, SA1, AV2, AV1, CE1, KR1. The average value of the anthropogenic pressure index (Pave) for each cluster was Pave ¼ 4.0 and 2.1, respectively. Moreover, the first cluster was characterized by higher metal concentrations (Table 8). These statistical results suggest that the clusters are based on different anthropogenic pressure levels due to industrial and anthropogenic emissions. For milk samples, the clustering procedure highlights two groups of farms, AV1, AV3, SA1, KR4 and AV2, KR1, KR2, NA1, KR3, CE1, CE2 and an isolated element AV4. In this case, the values were similar for the two clusters, while for AV4 the Pave was lower. Additionally, the centroids of the two clusters were comparable, except for Cr concentrations. They were 0.08 and 0.27 mg/g dry weight for the two clusters,
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Table 6 Results of explorative statistical analysis and data reported in the literature for heavy metal levels in tissue samples
Table 7 Mean values of concentrations measured in summer and in winter Element in mg/g dry wt
Element in mg/g dry weight
Liver
N m r SD VC%
Muscle
N m r SD VC%
Kidney
N m r SD VC%
Fat
N m r SD VC%
Pb
Mn
Hg
0.33 0.16 0.62 0.25 77
1.5 1.4 1.6 0.1 7
15.3 12.9 19.2 3.4 22
3 0.0051 0.0032 0.0084 0.0029 57
0.16 0.11 0.19 0.03 20
1.6 1.3 2.2 0.4 25
1.3 1.2 1.4 0.1 5
4 0.0036 0.0015 0.0066 0.0022 60
6.71 4.24 10.87 2.88 43
2.0 1.5 2.2 0.3 16
7.0 5.4 9.1 1.7 25
4 0.0085 0.0053 0.0103 0.0023 27
0.31 0.04 0.79 0.34 112
0.31 0.07 0.74 0.30 99
0.7 0.3 1.2 0.4 57
4 0.0075 0.0017 0.0177 0.0073 98
Data reported in the literature Bovine samples from a polluted sitea Liver m 5.1 Muscle m 0.6 Kidney m 10.3 Bovine samples from a control sitea Liver m 1.7 Muscle m 0.2 Kidney m 2.2 N, sample number; m, mean value; r, range; SD, standard deviation; VC%, percentage variation coefficient. a Sedki et al. (2003).
Cr
Pb
Mn
Hg
Summer (n ¼ 3) Winter (n ¼ 7)
0.89 (0.83) 0.36 (0.51)
6 (2.7) 7 (13)
1.1 (0.3) 1.3 (1.4)
65 (33) 116 (103)
0.007 (0.002) 0.005 (0.007)
Milk
Summer (n ¼ 5) Winter (n ¼ 8)
0.05 (0.01) 0.07 (0.02)
0.15 (0.18) 0.23 (0.08)
0.17 (0.06) 0.22 (0.06)
0.11 (0.03) 0.13 (0.03)
0.002 (0.001) 0.003 (0.001)
Ricotta
Summer (n ¼ 4) Winter (n ¼ 8)
0.08 (0.03) 0.12 (0.07)
0.13 (0.09) 0.33 (0.2)
0.36 (0.25) 0.47 (0.22)
0.19 (0.1) 0.23 (0.11)
0.001 (0.001) 0.001 (0.001)
Cheese
Summer (n ¼ 2) Winter (n ¼ 8)
0.08 (0.01) 0.12 (0.12)
0.38 (0.05) 0.43 (0.07)
0.77 (0.55) 0.24 (0.14)
0.26 (0.02) 0.39 (0.19)
0.002 (0.001) 0.008 (0.007)
Mature cheese
Summer (n ¼ 2) Winter (n ¼ 6)
0.12 (0.02) 0.1 (0.04)
0.42 (0.11) 0.48 (0.11)
0.81 (0.37) 0.5 (0.21)
0.67 (0.62) 0.37 (0.14)
0.03 (0.04) 0.01 (0.007)
For each value, the standard deviation is shown in parentheses. The cases in which the means are significantly different are reported in bold.
100 80 60 40 20 0 Fodder
(A)
Milk
Cd
Cr
Mn
Ricotta Pb
Hg
100 80
cp(%)
respectively, and 0.48 mg/g dry weight for the isolated element (farm AV4). Thus, the statistical analysis of milk samples allows us to classify the farms on the basis of Cr levels alone. For ricotta samples, we obtained two clusters, AV3, KR1, KR2, KR3, KR4, CE2 and AV1, AV2, NA1, CE1. The Pave values for these were 3.6 and 1.0, respectively. In this case the multivariate procedure divided the farms on the basis of Cr and Pb levels, but we observed higher heavy metal concentrations in the farms for which the average value of anthropogenic pressure index was lower. The index G, instead, is not an exogenous variable characterizing the clusters; in fact, in all of the clusters there was no specific dominance of sites with similar microclimatic features.
Cd Fodder
cp(%)
Cd
60 40 20 0
(B)
Fodder
Milk Cd
Cr
Cheese Mn
Pb
Mature chese Hg
Fig. 2. Percentage persistence coefficient values for the fodder–milk–ricotta chain (A) and for the fodder–milk–cheese–mature cheese chain (B).
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5. Conclusions This paper shows the analysis of relationships among five heavy metal concentrations measured in a variety of samples (ovine food, milk, dairy products, and tissues) collected in ovine farms of Southern Italy. The analyzed samples are representative of different links in the food chain. Our approach allows us both to investigate the distribution of the metals along this food chain, taking into account different spatial and temporal patterns, and to evaluate the influence of anthropogenic emissions in the surrounding areas. The study is based on the application of different statistical methods (univariate, bivariate, and multivariate techniques) and on the identification of some indicators (variation coefficient, persistence coefficient, cluster centroids) able to explain the results. The analysis of the variation coefficient allows us to highlight that the variability of heavy metal concentrations in milk and in dairy products is lower than in fodder samples. This result is ascribable to the influence of ovine metal metabolism. Moreover, the increase of
120 Leaver
100
Muscle Kidney
cp(%)
80
Fat 60 40 20 0 Cd
Mn
Pb
Hg
Fig. 3. Percentage persistence coefficient values for the fodder–target organ chains.
55
spatial spread observed in dairy products, with respect to milk, may be related both to different production processes and to different environmental conditions. Regarding the temporal patterns, the seasonal analysis did not point out significant differences in the contamination levels among samples collected during different periods (winter or summer). The analysis of the persistence coefficient highlights the relationships among the metals along the different chain links. Particularly, we note that Mn shows, in all of the examined cases, the lowest values of persistence, while the highest values are shown by Hg. The concentration variations from milk to products depend both on the kind of diary product (cheese or ricotta) and on production processes. Regarding organs and tissues analysis, although the number of examined samples is low, the data showed Cd, Pb, and Hg to have concentrations on the same magnitude of order in both tissues and in fodder. This is particularly true for liver and kidney; they show high values for the persistence coefficient, confirming that these target organs play a specific role as metal bioaccumulators. A comparison among the values of the persistence coefficient observed in all of the examined chains highlights that in the milk and dairy product chains the influence of feed contamination is less evident than in the tissue chains. Regarding the multivariate analysis, the clustering on the metal concentrations measured in the fodder samples suggests that the farm classification is mainly determined by different anthropogenic pressure levels due to industrial and anthropogenic emissions, in accord with the analysis based on the VC% values. On the contrary, in the case of the milk samples, the farm subgroups have similar Pave values and similar centroids, except for the Cr values. Chromium assumes the role of the discriminant element among the samples. This result, combined with the discussed VC% patterns, seems to indicate that the characteristics of milk samples
Table 8 Centroid table Element in mg/g dry wt Cd
Cr
Pb
Mn
Hg
Pave
Fodder samples Cluster1: KR2, KR4, NA1 Cluster2: AV12, AV2, KR1, SA1, CE1, CE2
0.60 0.52
14.4 2.8
1.8 0.9
218 53
0.0097 0.0037
4.0 1.8
Milk samples Cluster1: AV1, AV3, SA1, KR4 Cluster2: AV2, KR1, KR2, NA1, KR3, CE1, CE2 AV4
0.052 0.062 0.070
0.08 0.27 0.48
0.19 0.19 0.22
0.12 0.12 0.13
0.0027 0.0023 0.0026
3.0 2.1 1.0
Ricotta samples Cluster1: AV3, KR1, KR2, SA1, KR3, KR4, CE2 Cluster2: AV1, AV2, NA1, CE1
0.09 0.12
0.21 0.34
0.33 0.61
0.22 0.22
0.0010 0.0014
3.6 1.0
The mean values of the P index (Pave) are shown in the last column.
ARTICLE IN PRESS 56
R. Caggiano et al. / Environmental Research 99 (2005) 48–57
Fig. 4. Results of the clustering procedure. For each data matrix (fodder, milk, and ricotta), the clusters are represented in grey.
are determined mainly by ovine features and that the external conditions have a minor role. For ricotta samples, we obtain a farm classification that reflects both of the aspects. In this case the multivariate procedure divides the samples on the basis of Cr and Pb levels, obtaining subgroups with different Pave values, but the higher heavy metal concentrations correspond to the lower values of the anthropogenic pressure index. This result confirms the low dominance of external sources of contamination on metal levels in
milk and dairy products. In the end, we found that the independence of metal levels from site microclimatic features was verified by all of our samples.
Acknowledgments We thank Prof. G. Paino for helpful discussions. Furthermore, we thank the ASLs and the farms that have kindly cooperated with us.
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