1. Introduction

2. Materials and Methods

2.1. Sample Collection

The milk samples analyzed in this study (n =142) were produced by cows raised in extensive and semi-intensive dairy farm systems in the mesoregion of Agreste de Pernambuco (Garanhuns microregion). The samples were collected between September and December 2018. The distance between each farm and local, state and/or federal roads was determined. The proximity of the farms to major roads was considered close when the distance was less than 3 km and distant otherwise. Regarding effluents, the influence of hydrographic features (large and small rivers, as well as springs and other sources such as wells and dams) on the farms and access to these sources by the cows was taken into account. The milking method was classified as manual (performed manually by a responsible worker) or automatic (carried out by means of milking machinery). Raw milk samples (15 ml each) were obtained from cows in the early lactation stage (11 to 100 days postpartum). The first 3 jets of milk were discarded and the next 15 ml was collected. Samples were stored at 4 °C in 15 ml sterile plastic containers with a lid (Falcon®) and sent to the Clinical Pathology Laboratory of the Garanhuns Cattle Clinic (UFRPE), where they were then stored at -20 °C until analysis.

Figure 1. Municipality of Garanhuns and milk production in the Pernambuco dairy basin.

Figure 1. Municipality of Garanhuns and milk production in the Pernambuco dairy basin.

3. Results and Discussion

3.1. Toxic and Essential Trace Element Concentrations in Milk

The toxic and essential trace element concentrations determined in the milk samples are presented in Table 1. The concentrations of these elements determined in other studies around the world are shown in Table 2 for comparative purposes.

The mean Cd and Pb concentrations determined in the present study were 0.007 and 0.043 mg/L respectively. The mean Cd concentrations were low and similar to those reported in other studies in unpolluted areas (generally below 0.010 mg/L) and much lower than those reported in previous studies in Brazil [14,20] and in polluted areas of developing countries in Asia, Africa and South America (see Table 2). However, the concentrations of Pb are higher than reported in other recent studies conducted in relatively unpolluted areas from Europe or North America (see Table 2), but lower than reported in previous studies in Brazil [13,14,15,20,21]. The Pb concentrations determined in the present study are similar to those determined in some polluted regions of Iran [8] and Peru [22], but much lower than reported in other polluted areas in Asia or Africa (see Table 2).

Considering the applicable legislation, the Pb content of 29.6% of the samples (42/142) was above the limit permitted under Brazilian law (Decree n. 55871/65), which is 0.05 mg/L [12], and that of 97% of the samples (138/142) exceeded the limit established in the European Union [11] and Codex Alimentarius Commission and the WHO, i.e. 0.020 mg/L. By contrast, the Cd content of all samples was lower than 0.02 mg/L, the limit established by Brazilian law [12].

Previous studies conducted in Brasilia [23], Paraná [14] and Sao Luis [15] also reported Pb concentrations higher than the maximum limit. The Pb and Cd contents of milk depend on the proximity of polluted areas, crowded roads, level of industrialization (see Table 2) and also are influenced by the control and legislation limits [3]. The concentrations determined in Western and Central Europe, USA and Canada are below levels considered to represent a risk, unlike in Brazil, Mexico, Peru, some parts of Asia or Africa and polluted areas of Eastern Europe [3,24]. Recent studies have reported Pb and Cd concentrations in milk higher than respectively 60 and 12 mg/kg, in some parts of India [3], Pb concentrations in milk exceeding 13 mg/kg in Indonesia [25].

Considering the trace elements, the mean concentrations of Cu (0.020 mg/L), Fe (0.055 mg/L) and Zn (0.621 mg/L) were generally lower than reported in other studies (see Table 2) and can be considered within the deficient range for cow milk according to Puls [26] (deficient ranges are Cu: 0.010-0.020; Fe: <0.2 and Zn: <0.5 mg/L; adequate ranges are Cu: 0.05-0.6; Fe: 0.2-0.63 and Zn: 2.3-4 mg/L). These results indicate that milk produced in this region is not a good source of trace elements for the local population. The mean levels of Cu and Fe in raw cow’s milk samples across the world ranged from 0.0136-36 mg/L and from 0.33-16.4 mg/L, respectively [3]. The presence of heavy metals such as Pb and Cd is associated with changes in the trace mineral profile of milk and negatively affects the nutritional quality of the product, e.g. reducing the Fe content [2]. Trace element deficiencies in livestock are prevalent in different regions of Brazil [27], especially in several semi-arid climate areas. During the dry season, the pastures are generally overgrazed, leading to mineral deficiencies in the herds [16].

Table 2. Toxic and essential trace element concentrations (in mg/L) in cow’s milk determined in previous studies. Values are arithmetic meansm expressed in mg/kg or mg/l wet weight.

Table 2. Toxic and essential trace element concentrations (in mg/L) in cow’s milk determined in previous studies. Values are arithmetic meansm expressed in mg/kg or mg/l wet weight.

Country	Region	Cd	Pb	Cu	Fe	Zn	Reference
Algeria	Guelma area. Polluted area	0.03	0.94	0.14	0.76	4.02	[28]
Algeria	Polluted area	0.030		0.239	1.43	5.98	[29]
Argentina	Rural areas Southeast of Córdoba		0.0023	0.0380	0.855	1.800	[30]
Bangladesh	Dairy Farms	0.024	0.015	0.064	0.333		[31]
	Small household	0.047	0.012	0.127	0.631
Brazil	Paraná state. Pasteurized	0.018	0.281				[14]
	Paraná state. In Natura	0.031	0.181
Brazil	State of Goiás (supermarkets)	0.05	0.24	0.49	0.96	3.73	[20]
Brazil	Industrial area	0.002	ND	0.063		3.87	[32]
	Non-industrial area	0.003	0.003	0.068		3.15
Brazil	Vale of Paraíba region		0.23	1.73	1.05	4.59	[13]
China	Industrial	0.00015	0.00286				[33]
	Unpolluted	0.00013	0.00232
China	Samples from Shandong and Shaanxi cities	0.00007	0.0014	0.0324	0.352	3.234	[34]
China	Ten main milk producing areas in China	0.00005	0.00175				[35]
Croatia	Four unpolluted areas	ND	ND-0.0071	0.06 0.07	0.26-0.30	3.7-4.8	[36]
Egypt	Beni-Suef governorate	0.051	0.214	0.0953	8.994	6.29	[37]
England	Southern England. Conventional farmland			0.0606	2.03	5.00	[38]
	Southern England. Organic farmland			0.0524	0.66	4.51
Ethiopia	Kosoye Amba-Rass, Tana-Abo and Nara-Awdarda, North Gondar, Amhara Regional State	0.29	0.15	1.12		3.02	[39]
Hungary	Highway-area	0.005	0.025	0.336	0.797	1.494	[40]
	Non-polluted	ND	0.012	0.137	0.788	2.241
India	Mining areas		0.09-0.13	0.31-0.51	8.8-11.4	1.22-1.04	[41]
India	Ladakh, a trans-Himalayan high altitude region	0.007-0.009	0.005-0.006	0.23-0.30	3.55-4.91	1.99-3.76	[42]
India	Industrial areas	0.02-0.07	0.05-0.20	0.07-0.35		1.22-20.94	[43]
India	Industrial area	0.096	0.480	0.090	3.97	6.09	[2]
	Non-industrial area	0.033	0.250	0.101	5.10	3.95
Indonesia	City area, Padang	ND	13.6-20.6	1.17-2.17		28.8-53.1	[25]
Iran	Farms close to petroleum industries	0.0047	0.047				[8]
Iran	Arak city	0.00395	0.0125				[44]
Iran	Industrial regions of Iran	0.00111	0.0140	0.427		0.571	[45]
Iran	Lorestan province	0.10	2.72	0.14		3.07	[46]
Italy	Industrial area	ND	0.02	0.07-0.08	14.5-16.8	2.21-2.86	[47]
Italy	Calabria	0.0002	0.001	0.003		2.02	[1]
Kazakhstan	Almaty region. Unpolluted	0.0027	0.0045				[48]
Korea	Supermarkets	0.00238	0.00335	0.3834		4.754	[49]
Kosovo	Rural areas	0.001	0.0017	0.018	0.426	3.151	[50]
Mexico	Areas irrigated with wastewater		0.03	0.01		0.71	[9]
Mexico	Puebla, industrial wastewater	0.002	0.024	0.030			[51]
Moscow	Moscow region	0.004-0.011	0.075-0.110	0.11-0.21	0.55-0.82	1.21-141	[52]
Pakistan	Sargodha. Near traffic road	0.04–0.3	0.3–0.8				[53]
Peru	Near metallurgical complex	0.020	0.058				[22]
Peru	Mining-metallurgical industries	0.018	0.577				[10]
Poland	Lubuskie Province Organic farms	0.0035-0.0040	0.037-0.041	0.038-0.045	0.198-0.258	3.02-3.28	[54]
Poland	Low-level industrialization	<0.004	0.012	0.360		4.83	[55]
	High-level industrialization	<0.004	0.234	1.33		15.84
Romania	Intermediate-level industrial	0.0039	0.120	2.4		4.8	[56]
	Intensive industrial	0.007	0.577	0.837		4.8
	Small cattle farms	0.007	0.024	0.265		3.18
Romania	No industry	0.006	0.066	0.30		2.5	[57]
Serbia	Novi Sad (Vojvodina) market	0.00349	0.0754	0.118			[58]
Spain	Organic farms	0.000135	0.000653	0.041	0.425	3.326	[59]
	Conventional farms	0.000098	0.000516	0.051	0.395	3.639
	Conventional (supermarket)	0.000087	0.000267	0.069	0.351	3.933
Spain	Unpolluted region. Organic	ND	0.000519	0.039	0.271	2.851	[7]
	Unpolluted region. Conventional	ND	0.000389	0.048	0.301	3.368
Spain	Farms near mining and industrial area and highway traffic	<0.002	0.004				[60]
Sri Lanka	Four agro-climatic zones	0.001–0.002	0.005–0.02	0.02–0.12	0.49–3.15	1.49-2.93	[61]
Turkey	Local markets in the city of Edirne			0.138	3.1	3.4	[62]
Turkey	Iğdır City	0.0001-0.004	0.050	0.08-1.80		2.21-32.5	[63]
Turkey	Close to highways	0.39	1.85	0.62	4.2	1.85	[64]
Zambia	Farms near mining area		0.002				[65]

ND: not detected.

3.2. Effects of Proximity to Main Roads, Presence of Effluents and Milking Method on Toxic and Trace Element Concentrations in Milk

A general linear model was applied to the data in order to evaluate the effect of factors that potentially influence the toxic and trace element concentrations in milk samples (proximity of the farm to major roads, presence of effluents in the vicinity of farm and the milking method) The results are presented in Table 3, and it can be seen proximity to major roads (R) had a significant influence on the concentrations of Pb, Cd, and Cu in the milk samples. The presence of effluents in the vicinity of farm (E) only influenced the Cu concentration, while the milking method (M) did not influence the metallic profile of the samples.

The proximity of the farm to major roads was the most important factor in the analysis, exerting a significant effect on the Pb, Cd and Cu concentrations in milk, which were 90, 94 and 32 % higher in the milk samples from farms close to major roads than in milk samples from farms distant from major roads (Figure 1). The influence of the proximity to major roads on toxic element accumulation in milk, blood, water, soils, forage and other food products is well known [40,53,66]. Kodrik et al. [40] reported significantly higher levels of Cd (0.005 vs. ND mg/L), Pb (0.025 vs. 0.012 mg/L) and Cu (0.336 vs. 0.137 mg/L) in cow’s milk originating from traffic-intensive areas in Hungary, whereas Fe concentrations (0.797 vs. 0.788 mg/L) were similar to those in milk produced in unpolluted green areas and the Zn concentrations were lower in the former than in the latter (1.494 vs. 2.241 mg/L). Tahir et al. [53]reported high levels of Cd (0.04-0.3 mg/L) and Pb (0.3-0.8 mg/L) in cow’s milk in Pakistan, which were attributed to Cd- and Pb-contaminated feed, air pollution and drinking water contaminated by dust from areas close to roads. Similar results were reported by Bigucu et al. [64] in areas of Turkey close to major roads. The levels of contamination in the milk were also higher in these studies than in the present study.

Figure 1. Box and whiskers plot showing the effect of proximity of farms to main roads, the presence of effluents and milking method on toxic and trace element concentrations in milk (in mg/L).

Figure 1. Box and whiskers plot showing the effect of proximity of farms to main roads, the presence of effluents and milking method on toxic and trace element concentrations in milk (in mg/L).

Leaded petrol has caused more exposure to Pb than any other source worldwide, contaminating air, dust, soil, drinking water and food crops, and it has caused harmfully high human blood Pb levels around the world, especially in children [67]Lead persists in the environment and can bioaccumulate in bodies, with long-term effects after the initial source of Pb has disappeared [67]. Some decades after Pb was banned in petrol, residues of this metal in milk have largely decreased, but emissions of toxic metals associated with motor vehicles are still considered among the most important sources of heavy metals in the environment, and high concentrations of heavy metals are found in soils near roads/highways [68]. Monitoring heavy metal concentrations in roadside environments therefore remains of great importance.

The presence of effluents was also found to be a significant factor regarding the Cu of the milk, although samples from farms affected by effluents had lower Cu concentrations (23%) than samples from farms not affected by effluents. No significant interactions between the presence of effluents and proximity to roads were found, and milk samples from farms close to and distant from main roads had lower Cu contents when they were close to sources of effluents. Concentrations of Cu in milk are known to be influenced by the environment, industrialization and anthropogenic activities. A large part of the Pernambuco hydrographic basin receives industrial effluents, and the water cannot be used to supply the city, as it contains toxic elements, and the rivers in these areas are contaminated and affected by anthropogenic activities. As in the present study, Ogundiran et al. [69]reported that the Cu content was significantly higher in milk from cows reared in an industrialized area than in milk from cows reared in unpolluted areas. These findings suggest that other factors/trace elements present in the effluents may interact negatively with the Cu metabolism in cows, leading to a significant reduction in the Cu concentration in the milk. For example, complex non-competitive negative interactions between Cu, sulphur and molybdenum and competitive interactions with Cd and Zn for the metallothionein binding sites are known to occur in ruminants [6]Other interactions between Cu and Cd [70,71] and Cu and Pb [72] have also been described in cattle. Increased levels of Cd and Pb interfere with the metabolism of the essential trace elements, in particular with metabolism of Cu, with calves at risk of Cu deficiency [71]Such interactions are sometimes difficult to interpret as they depend on the chemical form and relative concentrations of the elements in the environment and can involve more than two elements [2,72].

3.3. Chemometric Analysis: PCA and HCA

Chemometric analysis can be used for detailed examination of large sets of data, enabling visualization of complex interactions between samples, variables and among samples and variables. Latent structures and relationships residing in the data matrix are commonly studied by means of two display chemometric techniques: PCA and HCA.

PCA was used for the primary evaluation of the 5-dimension data set. PCA transforms the autoscaled data matrix X_142x5 into a product of two matrices: the score matrix S145xPC, which includes information about the samples, and the loadings matrix LPCxPC, which includes information related to the variables. When the number of principal components (PC) selected is smaller than the number of original variables, PCA produces visualization of the data matrix in a reduced dimension, simplifies the original problem and enables examination of the relationships between samples and between variables through score and loadings plots, respectively [19].

In the present study, PCA was applied to the original autoscaled data matrix, and the first two PCs were considered sufficient to represent all data. The first two principal components explained 75.38% of the total variance; the remaining PCs yielded eigenvalues <1, indicating poor information content (Table 4).

This enabled evaluation of the whole data set by using a 2D-score (or loading) plot, where the samples (or variables) are represented in the space defined by the first two principal components. Examination of the sample scores in this plot produced some interesting results. A natural separation of the samples into two groups according to the proximity of the farms to major roads was detected (Figure 2a), implying an evident influence of the traffic emissions in the metal content of the milk. Despite this “natural” separation, there was also some degree of overlap between the two categories in the 5-multidimensional space of the variables, as seen in the score-plot.

Figure 2. 2D-score plot of the samples obtained by PCA according to (a), the proximity the farms to major roads, (b) the presence of effluents and (c) the milking method.

Figure 2. 2D-score plot of the samples obtained by PCA according to (a), the proximity the farms to major roads, (b) the presence of effluents and (c) the milking method.

The second step of chemometric analysis consisted of using HCA. This unsupervised chemometric procedure searches for groups of samples (or variables) in the multidimensional space. It is based in an algorithm that arranges similar samples (or variables) into groups called clusters. The similarity between objects is calculated on the basis of the distance that separates them, considering that near samples in the 5-dimensional space of the variables will be very similar to each other. In the present case, the similarity was measured as the squared Euclidean distance, and the Ward method was used as agglomerative algorithm procedure to identify clusters [73]. The dendrogram of the milk samples obtained when HCA was applied to the complete data set in the autoscaled X_142x5 matrix can be seen in Figure 4. Examination of the dendrogram revealed the presence of two clusters of milk samples in the 5-dimensional space defined by the metal variables. The first cluster (A) is composed of samples from farms distant from major roads, while the second (B) mainly includes samples from farms close to major roads. The presence of samples near roads in cluster A and the appearance of samples far from roads in cluster B demonstrate some degree of overlap between clusters previously indicated by PCA.

Figure 3. Biplot of the whole data set obtained by PCA. Samples are coded according to the proximity of major roads.

Figure 3. Biplot of the whole data set obtained by PCA. Samples are coded according to the proximity of major roads.

This separation in two clear groups does not occur for either the milking type or the effluent factors, thus confirming the conclusion reached in the PCA that the most significant factor affecting the metal content of the samples is the traffic in the proximity of livestock farms. Moreover, the group of milk samples from farms far from major roads were more similar to each other than the group of milk samples from farms close to major roads were (Figure 4). This was an expected result as the distance of the farms from major roads was variable. It has been reported that levels of heavy metal contamination (Pb and Cd are the most widely studied) in both soils and forage decreases to background levels with increasing distance on both sides of major roads [53,67]. Cattle and other animals that are grazed close to roads have been shown to have higher blood Pb levels than animals housed indoors [2,74], and the significant correlation between blood and milk Pb levels increases the potential human exposure[2,74]. One of the most important causes of high Pb content in milk from rural, relatively unpolluted areas may be the proximity to major roads or transhumance along roads and/or motorways [64,75].

Figure 4. Dendrogram of the samples according to the proximity of major roads obtained by HCA (Squared Euclidean distance and Ward agglomerative method).

Figure 4. Dendrogram of the samples according to the proximity of major roads obtained by HCA (Squared Euclidean distance and Ward agglomerative method).

Additionally, HCA was used to evaluate associations between variables, and in this case two clusters of variables were also detected (Figure 5): the first included toxic metals Cd and Pb and the second comprised the essential trace elements. The variable arrangement may be directly linked to the differences in these metals according to the presence or absence of any major roads in the proximity of the farm where the milk samples were collected. In the plots, the metals are ordered from left to right on the basis of their capacity to distinguish between samples near and far from main roads. The variables in the first cluster (Cd and Pb) showed clearly different levels for samples on the basis of the road factor (with higher levels in milk from farms near main roads). Some differences in Cu concentrations were also observed for both classes, but with lower discriminatory capacity, while Fe and Zn showed similar or equal ranges. These results are consistent with those obtained by PCA and when the GLM was considered for testing the effect of the road factor in the metal content of milk samples.

Taking into account the high concentrations of Pb in almost 30% of the milk samples analyzed, as well as the low content of other essential mineral elements, the consumption of milk from this region may represent a health risk to the population. More extensive studies that include larger sample sizes and different metals, and that determine Pb levels in the rock and soil should be conducted to clarify the risk associated with consumption of milk in the study region.

4. Conclusions

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