INCIDENCE OF CONTAMINATION WITH PERSISTENT ORGANIC ... · Chapter 1. Persistent organic pollutants...

UNIVERSITY OF AGRICULTURAL SCIENCE AND VETERINATY MEDICINE CLUJ-NAPOCA

DOCTORAL SCHOOL FACULTY OF ANIMAL SCIENCE AND BIOTECHNOLOGY

Eng. Rowena-Ana CHELEMAN

INCIDENCE OF CONTAMINATION WITH

PERSISTENT ORGANIC POLLUTANTS IN THE

AGRI-FOOD CHAIN

(SUMMARY OF Ph.D THESIS)

Scientific Coordinator:

Prof. dr. MARIA TOFAN Ă

CLUJ-NAPOCA

2011

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CONTENTS

INTRODUCTION .................................................................................................................. IV 1. AIM AND OBJECTIVES OF THE THESIS .................................................................... VI 2. MATERIAL AND METHODS .......................................................................................... VI

2.1. MATERIAL ........................................................................................................... VI 2.1.1. Collection of feed samples ....................................................................... VI 2.1.2. Experimental design for feed samples ..................................................... VII 2.1.3. Collection of soil, vegetation and milk samples ..................................... VIII 2.1.4. Experimental design for environmental and milk samples ........................ IX

2.2. ANALYTICAL METHODS .................................................................................. IX 2.2.1. Dioxin and PCBs determination from feed using a high resolution gas-

chromatographic method - HRGC/HRMS .......................................................................... X 2.2.2. Determination of polychlorinated biphenyls (PCBs) content from soil,

vegetation, milk by GC-ECD ............................................................................................. XI 3. RESULTS AND DISCUSSION REGARDING THE INFLUENCE OF SOME MANUFACTURING PARAMETERS ON THE CONTENT OF DIOXINS AND PCBS IN FEED..................................................................................................................................... XII

3.1. PHYSICO-CHEMICAL PARAMETER DETERMINATION OF FEED .............. XII 3.2. IDENTIFICATION AND DISTRIBUTION OF DIOXINS AND PCBS FROM FEEXII

3.2.1. Distribution of PCDD/F and PCB in raw material ................................... XII 3.2.2. Distribution of PCDD/Fs and PCBs în compound feed .......................... XIV

3.3. INFLUENCE OF SOME HEAT TREATMENTS USED IN THE PRODUCTION OF COMPOUND FEED ON THE LEVELS OF DIOXINS AND POLYCHLORINATED BIPHENYLS .......................................................................................................................... XV

3.3.1. Variation of dioxins and PCBs concentration in raw material before and after the drying process .................................................................................................... XV

3.3.2. Variation of dioxins and PCBs concentration in compound feed before and after granulation process ................................................................................................ XVII

4. TRACEABILITY STUDY OF POLYCHLORINATED BIPHENYLS IN AGRI-FOOD CHAIN ................................................................................................................................. XIX

4.1. INCIDENCE OF POLYCHLORINATED BIPHENYLS IN SOIL SAMPLES .... XIX 4.1.1. Water content determination from soil samples ..................................... XIX 4.1.2. Identification and pattern of polychlorinated biphenyls in soil ................ XX 4.1.3. Variation of PCBs content in soil samples from the studied georaphical

areas ............................................................................................................................... XXI 4.2. INCIDENCE OF POLYCHLORINATED BIPHENYLS IN VEGETATION ..... XXII

4.2.1. Moisture content determination from vegetation samples ..................... XXII 4.2.2. Identification and pattern of polychlorinated biphenyls in vegetation

samples ........................................................................................................................ XXIII 4.2.3. Variation of PCBs content in vegetation samples from different areas . XXIII

4.3. INCIDENCE OF POLYCHLORINATED BIPHENYLS IN MILK .................. XXVI 4.3.1. Determination of some physicochemical parameters in milk samples using

the LactoStar analyzer ................................................................................................. XXVI 4.3.2. Identification and pattern of polychlorinated biphenyls in milk samplesXXVII 4.3.3. Variation of PCBs content in milk samples from different areas ........ XXVII

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5. CORRELATION OF SOME ANALYZED PARAMETERS WITH DIO XIN AND POLYCHLORINATED BIPHENYLS ......................................................................... XXVIII

5.1. CORRELATION OF SOME PARAMETERS WITH DIOXIN AND POLYCHLORINATED BIPHENYLS CONTENT IN ANALYZED FEED SAMPLES ..... XXIX

5.2. CORRELATION OF SOME PARAMETERS AND POLYCHLORINATED BIPHENYLS IN ANALYZED ENVIRONMENTAL SAMPLES ...................................... XXXI

5.3. CORRELATION OF SOME PARAMETERS AND POLYCHLORINATED BIPHENYLS IN ANALYZED MILK SAMPLES ............................................................ XXXIII 6. GENERAL CONCLUSIONS .................................................................................... XXXIV SELECTED REFFERENCES ..................................................................................... XXXVII

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INTRODUCTION

Persistant Organic Pollutants (POPs) are chemical substances that persist for long periods in the environment, bioaccumulate in living organisms and are toxic for the environment, animals and people.

These pollutants have raised a global concern so that through the United Nations Environment Programme was initiated the Stockholm Convention in Mai 2001. The main objective of the Convention was to protect the environment and human health by reducing and eliminating, where possible, the 12 “dirty dozen” persistent organic pollutants (pesticide, industrial chemicals and unintentionally produced byproducts). Among these only polychlorinated dibenzo-p-dioxins and polychlorinated furans are not produced by humans, but are secondary substances that are formed during combutstion processes and uncontrolled burning in nature (INEP, 2008).

The majore route by which humans are exposed to POPs contamination is through ingestion of contaminated food, approximately 90% of the cases, of whici 60 – 80 % are due to consumption of animal foods (fish, meat, milk) and 20–40 % due to consumption of processed food. The foods producing animals are expoused tor POPs through ingestion of contaminated feed (FERRARIO, 1996; PARZEFALL, 2002).

Following different medical studies it was established that there is a link between POPs and some human illnesses and disabilities: cancers and tumors, including the soft tissue sarcoma; neurological disorders, like attention deficit disorder, behavior problems, learning disabilities and impaired memory; immune suppression; reproductive disorders, like abnormal sperm, miscarriges, premature birth, short lactation periods, menstrual disorders; other diseases including increased incidence of type II diabetes, endometriosis, hepatitis and cirrhosis (ATSDR, 1998).

POPs are persistant, bioacumulative (they accumulate in the faty tissue of the living organisms), toxic, and volatile (they are semivolatile compounds, which can travel long distances by means of evaporation, air transport and condensation cycles), they are subject to long-range transport (“grasshopper” effect), and not least bioaccesability (RFI, 2001).

As a signatory to the Stockholm Convention, Romania was also, obligated to develop a National Implementation Plan, which was done through the project “Preparatory work in developing the National Implementation Plan of the Stockholm Convention on Perisitent Organic Pollutants (POPs) in Romania”, financed by the UNIDO/GEF, and approved by the governmental authorities (CADARIU, 2005).

Due to the ubiquitous of these pollutants, over the last decades researchers have tried to optimize different identification and quanification methods for POPs, so, currently for determining these pollutants from different matrix can be used chemical methods based on gas-chromatography (GC-ECD, GC/MS, HRGC/HRMS) and bilogogical methods (for example, cell and organ bioassay). Among gas-chromatographyc methods, high resoilution gas chromatography coupled with high resoilution mass spectrometry (HRGC/HRMS) is known as the “golden method” for the determination of POPs, like polychlorinated dibenzo-p-dioxins (PCDDs) and furans (PCDFs) (EPPE et all., 2004; PISKORSKA et al., 2004; GIZZI et. all., 2005; BOVEE, 2006; SROGI, 2007).

In Romania, the most studied POPs were the pesticides and polychlorinated biphenils (PCBs), compounds that were identified in matrixs like: soil, water, air, food, biological samples and so one. COVACI et al. (2001, 2003, 2006) in their publications on PCBs present results from the analyses of samples from the East, South and West part of the contry.

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The paper “Incidence of contamination with persistent organic pollutants in the agri-food chain” is conceved as a whole and follows the incidence of some persistent organic pollutants (dioxins and polychlorinated biphenyls) in the agri-food chain. This paper work is summarized in 8 chapters presented on 241 pages, with 53 tables, 60 figures and 30 annexes. In this thesis were cited 108 refferences, and during the doctoral studies some results were published in professional journals.

THESIS STRUCTURE The thesis is structured in two main parts, the first part including literature studies, and

the second part, experimental researches, results and discussion, recommendation, references and annexes.

FIRST PART – STATE OF KNOWLEDGE IN THE STUDIED FILE D, includes two chapters:

Chapter 1. Persistent organic pollutants – incidence in the agri-food chain, includes five subchapters where are presented the main characteristics of persistent organic pollutants (POP), toxicological aspects, regulation regarding these contaminants, as well as sources of POPs contamination of environment and food.

Chapter 2. Risk of contamination with persistent organic pollutants during feed production, includes three subchapters in which is presented the thechnology of compound feed production, sources of contamination with POPs (dioxins and polychlorinated biphenyls) of feed, and also, separation, identification and quantification methods used for these contaminants.

SECOUND PART – OWN RESERCHES, includs six chapters: Chapter 3. Aim and objectives of the thesis. Chapter 4. Material and methods, incuds two subchapters where are prezebted the

experimental designs, sample collection ares, and also, the researche methods used. Chapter 5. Influence of some manufacturing processes on the content of dioxins

and PCBs in feed, includes five subchapters which present the obtained results, discussions, and also, statistical analysis of the measured results and partial conclusions.

Chapter 6. Traceability study of polychlorinated biphenyls in agri-food chain, includs three subchapters which present the obtained results and discussions on each type of nalysed sample. Here are also included statistical analysis and partial conclusions.

Chapter 7. Correlation of some analyzed parameters with the content of dioxins and polychlorinated biphenyls, includes four subchapters where are presented correlations between some physic-chemical parameters, analyzed for for each sample, and the content of dioxins and PCBs identified and quantified.

Chapter 8. General conclusions, presents the general conclusions, recommendations, perspectives, and original contribution. Part two ends with consulted Refferences and Annexes.

The experiments in this thesis were conducted in three laboratories: Dioxin Laboratory of Ecological Chemistry Institute belonging to Helmholtz Researcher Center München in Germania, Food Quality and Safety Testimg Laboratory (LICSA) at the University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca and at the National researce-Development Institute for Criogenic and Isotopic Technology (ICSI), Râmnicu Vâlcea.

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1. AIM AND OBJECTIVES OF THE THESIS The aim of the thesis is to establish the incidence of contamination with persistent

organic pollutants in the agri-food chain, more precisely dioxins and polychlorinated biphenyls. After analyzing the literature, presented in Part I, have emerged several general and specific objectives to follow in order to achive the established aim: � OBJECTIVE 1 – Feed quality assessment in terms of contamination with persistent

organic pollutants dioxins and polychlorinated biphenyls. � OBJECTIVE 2 – Study regarding the incidence of persistent organic pollutants – PCBs –

from soil and vegetation in order to asses their traceability. � OBJECTIVE 3 Study regarding the incidence of persistent organic pollutants – PCBs –

from milk. � OBJECTIVE 4 – Correlation of some quality parameters of the analyzed samples with the

content of contaminant.

2. MATERIAL AND METHODS 2.1. MATERIAL

2.1.1. Collection of feed samples To asses the contamination level of feed, samples were collected from different

compound feed factories (FNC) and units that processes and stories cereals and oilseeds used as raw material in compound feed or as such for animals feeding.

To asses the way in which the gramunaltion process influences the content of dioxins and PCBs in pellets, samples were collected, for each type of feed, in two steps of the process: before heat treatment – input and after the heat treatment – output.

Table 1 Sampling protocol for compound feed

Unit code County

Sample code

Quantity (g)

Sample description

F1 Bihor

F1A-1 1 x 200 Finished product (output) for fattening calves (A-1). F1SemiA-1 1 x 200 Semi-finished (input) for productul A-1. F1B 1 x 200 Finished product (output) for hens for eggs phase I (B). F1SemiB 1 x 100 Semi-finished (input) for product B. � sample was

prepared in the laboratory according to the manufacturing recipe.

F1B-1 1 x 200 Finished product (output) for hens for eggs phase I (B-1). F1SemiB-1 1 x 200 Semi-finished (input) for product B-1. F1C-1 1 x 200 Finished product (output) for fat pig growth (C-1). F1SemiC-1 1 x 200 Semi-finished (input) for product C-1. F1G 1 x 200 Finished product (output) for finishing pigs (G). F1SemiG 1 x 200 Semi-finished (input) for product G. F1H 1 x 200 Finished product (output) for hens for eggs phase II (H). F1SemiH 1 x 200 Semi-finished (input) for product H. F1I 1 x 200 Finished product (output) for milk cows (I). F1SemiI 1 x 200 Semi-finished (input) for product I.

F2 Bihor

F2D 2 x 100 Finished product (output) for growing chicken (D). F2SemiD 2 x 100 Semi-finished (input) for product D. F2F 2 x 100 Finished product (output) for finishing pigs (F).

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Table 1 (continued) Unit code County

Sample code

Quantity (g)

Sample description

F2 Bihor

F2SemiF 2 x 100 Semi-finished (input) for product F.

F6 Bacău

F6J 1 x 100 Finished product (output) for prelaying hens (J). F6SemiJ 1 x 100 Semi-finished (input) for product J. F6K 1 x 100 Finished product (output) for hens for eggs phase I (K). F6SemiK 1 x 100 Semi-finished (input) for product K. F6M 1 x 100 Finished product (output) for finishing pigs (M). F6SemiM 1 x 100 Semi-finished (input) for product M. F6N 1 x 100 Finished product (output) for Emerlis Guenia cows (N). F6SemiN 1 x 100 Semi-finished (input) for product N. F6O 1 x 100 Finished product (output) for Astral Acord calves (O). F6SemiO 1 x 100 Semi-finished (input) for product O.

Units F3, F4 and F5 processes raw material (cereals and oilseeds) used in the feed

production industry and food industry. From these units were collected barley, maize and sunflower seed samples, data are presented in Table 2.

Table 2 Sampling protocol for feed raw material

County Sample code

Quantity (g) Sample description

Cluj F3OU 2 x 100 Dry barley (output). F3OV 2 x 100 Green barley (input).

Satu Mare F4PU 2 x 100 Dry maize (output). F4PV 2 x 100 Green maize (input).

Satu Mare F5FU 2 x 100 Dry sunflower seeds (output). F5FV 2 x 100 Green sunflower seeds (input).

2.1.2. Experimental design for feed samples The experimental design diagram corresponding to objective 1, feed quality evaluation

in terms of POPs contamination, is presented in figure 1. For each sample were determined physic-chemical parameters and the content of dioxins (PCDD/F) and polychlorinated biphenyls (PCB).

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Fig. 1. Experimental design for feed samples

2.1.3. Collection of soil, vegetation and milk samples In order to achive objectives 2 and 3 regarding contaminants traceability in agri-food

chain, were selected four areas for collecting the environmental samples (soil and vegetation) and food sample (milk). Table 3 presents the locations were the samples were collected, and also, the type of sample collected from each area.

Table 3 Sample collection area

No. Area code County Type of sample

1. S1Ho Cluj–Napoca, Cluj Soil, vegetation, milk 2. S2Mb Iclod, Cluj Soil, vegetation, milk 3. S3Lo Voivodeni, Sălaj Soil, vegetation, milk 4. S3Lb Gârda de Sus, Alba Soil, vegetation, milk

According to the studied literature it is known that for the investigated contaminants, the

highest levels are in the urban areas and decreases in the rural and remote areas. So the selection of the sampling areas was done according to this variable assigning for each area a letter to represent the supposed contamination degree: letter H represents a high contamination; letter M represints a medium contaminatio; letter L is for a low contamination. The smaller letters at the

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end of the code represent the type of milk collected from each area, so b stends for cows milk, and o for sheep milk. With this selection of the areas we want to follow the variation of contaminants depebding on the geographical location.

2.1.4. Experimental design for environmental and milk samples Figure 2 presints the diagram for the experimental design according to objectives 2 and 3

regarding the traceability of PCB compounds in the agri-food chain.

Fig. 2. Experimental design for environmental and milk samples

2.2. ANALYTICAL METHODS

Among the physic-chemical parameters were determined: moisture (raw feed materials,

compound feed, soil, vegetation and milk), crude fat (compound feed and milk), crude protein (compound feed and milk), crude ash (compound feed), mineral substances (compound feed and milk), lactose (milk), nonfat dry substances (milk).

The gas-chromatographyc methods used for the identification and quantification of dioxins and PCBs from different matrixs are presented in Table 4. In order to reduce the

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consumption of solvents and time needed to analyze one sample some modifications were made to the presented methods, so for the EPA methods (U.S. Environmental Protection Agency) modifications were made in the clean-up and purification steps; for SR ISO 10382 method was used Soxhlet extraction instead of solid-liquid extraction, and for method SR ISO 8260:2009 for milk was modified the clean-up and purification step. All the gas-chromatographyc methods used were validated at the laboratories were the analysis were done, by determining the performance parameters (recovery and liniarity).

Table 4 Chromatographyc methods

Analyzed compounds

Method Tehnică folosită

PCDD and PCDF EPA 1613 B. Tetra- through Octa-Chlorinated Dioxins and Furans by Isotope Dilution HRGC/HRMS.

HRGC/HRMS

PCB EPA 1668 A. Chlorinated Biphenyl Congeners in Water, Soil, Sediment, Biosolids, and Tissue by HRGC/HRMS.

HRGC/HRMS

PCB SR ISO 10382. Soil quality. Determination of organochlorine pesticides and polychlorinated biphenyls. Gas-chromatographyc method with electron capture detector.

GC-ECD

PCB SR ISO 8260:2009. Milk and milk products. Determination of organochlorine pesticide and polychlorinated biphenyls. Capilary gas-chromatographyc metod with electron capture detector.

GC-ECD

2.2.1. Dioxin and PCBs determination from feed using a high resolution gas-

chromatographic method - HRGC/HRMS This parte of the experiments was done during an internship at the Ecological Chemistry

Institute from Helmholtz München in Germany, benefiting from a DBU (Deutsche Bundesstiftung Umwelt) scholarship for 6 months. Determination of dioxins and PCBs from feed samples was done according to the specific procedures validated at the Dioxin Laboratory.

HRGC/HRMS technique is considered the “gold moethod” for the analysis of dioxins and PCBs from different matrix because this method has several advantages (ABAD, 2006; SROGI, 2007; BIANCO, 2008): high sensibility and low detection limits until pictograms in solid samples and fentograms for air samples; high selectivity, separates the compounds of interes from other coextracted compounds; high specificity, can distinguishe the isomers; accuracy and high precision.

Method principle. Determination of dioxins and PCBs from solid matrix (feed) by

isotop dilution from tetra through octa using high resolution gas-chromatography coupled with high resolution mass spectrometry (HRGC/HRMS).

Brifely, the method protocol involvs addition, before extraction, of internal standards, marked with radioactive carbon 13C; followed by sample extraction with a mixture of n-hexane:aceton (3:1, v/v); purification and partition on silicagel and active carbon column; purification on C18 cartridge for PCB fraction, respectively basic alumina column for PCDD/F fraction and measurement by HRGC/HRMS.

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Table 5 Parameters of the HRGC/HRMS gas-chromatogarphic method used to measure PCDD/F and

PCBs compounds

Parameter PCDD/F PCB

GC Model HP 6890 HP 5890 Seria II Column Rtx-Dioxin2 (60 m; 0,25 mm; 0,25 µm) ZB-MultiResidue-2 (30 m; 0,25 mm; 0,2

µm) Column temperature program

130 °C, 1.5 min, 45 °C/min, 205 °C, 5 min, 9 °C/min, 305 °C, 20 °C/min, 310 °C, 15 min.

100 °C, 1.5 min, 3 °C/min, 270 °C, 15 °C/min, 300 °C,10 min.

Carrier gas helium 1,5 ml/min helium 16 psi Injector CIS 4 (Gerstel); 120°C, 12 °C/s, 280°C,

5 min CIS 3 (Gerstel); 120 °C, 12 °C /s, 280 °C, 5 min

Autosampler A200S (CTC) MPS2 (Gerstel) Injection volume 1 µl 1 µl MS Model MAT 95S (Thermo Finnigan) MAT 95 (Thermo Finnigan) Ionisation mode EI, 50 eV, 260 °C EI, 47 eV, 260 °C Rezolution > 9000 > 8000 Detection mode SIM SIM

2.2.2. Determination of polychlorinated biphenyls (PCBs) content from soil, vegetation, milk by GC-ECD

Method principle. Determination of polychlorinated biphenyls content from soilid

matrix (soil and vegetation) and liquid matrix by gas-chromatography using an electron capture detector (GC-ECD).

Method protocol involves addition of internal standards; followed by Soxhlet extraction (for solid samples), liquid-liquid extraction (for milk samples) of the interesed compounds (dioxin-like and non dioxin-like polychlorinated biphenyls) using a solvent mixture of n-hexane:aceton (3:1, v/v); purification on silicagel and C18; determination by GC-ECD.

Table 6 GC-ECD parameters for the separation of PCB compounds

GC conditions Model Splite mode Injector temperature Carier gas Injection volume Column

450 GC Varian 1:10 260 °C Helium, 1 ml/min 1 µl CP-Sil 8CB (50m x 0.25mm x 0.25 µm).

Column temperature program

Initial temperature (°C)

Initial time (min)

Rate (°C/min)

Final temperature (°C)

Hold time (min)

130 2 2.5 290 5

Detector ECD Detector temperature Detection mode N2 flow

300 °C SIM 25 ml/min

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3. RESULTS AND DISCUSSION REGARDING THE INFLUENCE OF SOME MANUFACTURING PARAMETERS ON THE CONTENT OF DIOXINS AND PCBs IN

FEED The importance to asses the content of persistent organic pollutants (dioxins and PCBs)

from feed lies, on one side from their retention in the environment, and on the other side from the high toxicity.

3.1. PHYSICO-CHEMICAL PARAMETER DETERMINATION OF FEED

Mean values, for each category of compound feed (expressed in %) obtained at the

determination of water, total dry matter, crude protein, crude fat, cellulose, ash and nonazotic extractive substances (S.E.N.) are presented in Table 7.

Table 7

Quality parameters (mean value, %) for category of compound feed Feed type Moisture Total dry

matter Crude fat

Crude protein

Crude ash

Crude celulose

SEN

Cattle 11.77 88.22 5.60 15.55 8.05 6.20 52.82 Pigs 11.43 88.57 2.37 16.43 4.77 5.70 59.29 Poultry 10.40 89.59 3.73 17.37 6.10 6.24 56.14 3.2. IDENTIFICATION AND DISTRIBUTION OF DIOXINS AND PCBs FROM FEE

3.2.1. Distribution of PCDD/F and PCB in raw material Because dioxind and dioxin-like PCBs are present in matrix, moste of the times, as a

complex mixture in different proportions, current legislation stes maximum limits only for the sum of TEQ values, of the compuaunds PCDDs with PCDFs and PCDD/Fs with dioxin-like PCBs. The distribution pattern for dixins (PCDD/F) and polychlorinated biphenyls (PCBs) from raw feed materials is presented in figure 3.

A B

Fig. 3. Distribution of PCDD/Fs (A) and PCBs (B) congeners from raw material (pg/g product with 12% moisture)

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From the PCDD compounds, in the six samples analyzed, only two compounds were identified: 1,2,3,4,6,7,8-HpCDD (0.07 – 0.17 pg/g product with 12% moisture) and OCDD (0.18 – 0.88 pg/g product with 12% moisture). In the raw feed materials there were not identified the most toxic compounds from the dioxin group TCDD and TCDF. Regarding the furans (PCDFs), in the analyzed samples were identified eight compuands, except 1,2,3,7,8,9-HxCDF from the hexachlorofurans and 1,2,3,4,7,8,9-HpCDF from heptachlorofurans. In this class of compounds the higher values were for the congener OCDF (0.06 – 0.13 pg/g product with 12 % moisture). Dioxin-like PCBs had much lower values 0.12 – 15.0 pg/g product with 12% moisture. From the six indicators, PCB 28 was the most abundant with limits between 16.4 – 156.0 pg/g product with 12% moisture.

Congener distribution in three categories of raw feed material (barley, maize and floarea-soarelui) is presented in figure 4.

A B

Fig. 4. Distribution of PCDD/F (A) and PCBs (B) în barley, maize and sunflower (pg/g product with 12% moisture)

According to figure 4.A the sunflower samples had a higher content of contaminant

(1.37 pg/g product with 12% moisture) which can be correlated with the higher fat content, next were the barley and maize samples that had values between 0.6 pg/g product with 12% moisture, respectively 0.56 pg/g product with 12% moisture. From the PCBs point of view on the first place was the sunflower with concentrations between 0.11 – 96.37 pg/g (PCB 28), followed by barley with limites between 0.11 – 20.35 pg/g (PCB 28) and maize with limits between 0.16 – 105.8 pg/g (PCB 28). The highes contribution to the sum of six indicators PCB was for the compound PCB 28 (20%). PCB 77, 81, 126 and 169 are non-orto compounds, and PCB 105, 114, 118, 123, 156, 157, 167 and 189 are mono-orto compounds and for all are attributed a TEF factor because this have toxicological properties like dioxins, that is way they are also called dioxin-like PCBs or coplanar PCBs.

In order to compare with the maximum levels the TEQ (toxicity equivalence) values were calculated, so the concentration for dioxins and dioxin-like PCBs varied between 0.02 – 0.06 pg WHO-TEQ/g for barley; 0.01 – 0.02 pg WHO-TEQ/g for maize; respectivly 0.02 – 0.08 pg WHO-TEQ/g for sunflower. The maximum limit in raw feed material, according to Reg. (CE) No. 574/2011, for the sum of PCDD/Fs+ dioxin-like PCBs is 1.25 pg WHO-PCDD/F+dl-PCB-TEQ/g product with 12% moisture.

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3.2.2. Distribution of PCDD/Fs and PCBs în compound feed Pattern distribution for congeners PCDDs and PCDFs (dioxins) and polychlorinated

biphenyls (PCBs) is presented in figure 5.

A B

Fig. 5. Dioxins (PCDD and PCDF) and PCBs distribution in compound feed (pg/g feed with 12% moisture)

Compaing the pattern distribution for dioxins (PCDD and PCDF) obtained for the raw

feed material (Fig. 3.A) with that for compound feed (Fig. 5.A) we can see that for the compound feed were identified all the congeners from the dioxins group. OCDD congener had in the compound feed samples the higher concentration 0.06 – 12.9 pg/g feed with 12% moisture. As regard the PCBs, the six marker PCBs are present in all the compound feed samples, but from a toxicological point of view, more important are the dioxin-like PCBs. Figure 5.B shows that compound feed compared with raw material had a lower level of dioxin-like PCBs, but the distribution pattern is the same. The higher values are for PCB 28 (14.9 – 515 pg/g feed with 12% moisture) and PCB 52 (23.6 – 199 pg/g feed with 12% moisture).

A B

Fig. 6. Distribution of PCDD/Fs (A) and PCBs (B) congener in three types of compound feed (pg/g feed with 12% moisture)

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In figure 6.A is presented the distribution of PCDD and PCDF congeners in compound feed for cattle, pigs, and poultry. Compared with the literature for the analyzed feed from Romania we can see that the feed for cattle (0.04 – 5.09 pg/g furaj) had a higher dioxin content, followed by poultry feed (0.03 – 0.36 pg/g furaj) and pigs feed (0.02 – 0.3 pg/g furaj) (BYUNG-HOON et al., 2003; GURUGE et al., 2005).

For reporting with the legal maximum limits (0.75 pg WHO-PCDD/F-TEQ/g product with 12% moisture), the equivalence toxicity (TEQ) was also calculated for the compound feed, so the concentraion in the analyzed samples varied between 0.01 – 0.55 pg WHO-PCDD/F-TEQ/g product with 12% moisture. But comparing with the action limit (0.5 pg WHO-PCDD/F-TEQ/g product with 12% moisture) it can be seen that there was a sample that exceeded this limit, this was a semi-finisherd product from a poultry feed from unit F6, but after the granulation process the concentration in dioxins decreased to 0.04 pg WHO-PCDD/F-TEQ/g.

Figure 6.B presents the distribution pattern for PCBs compounds in three type of compound feed: cattle (0.15 – 139.27 pg/g feed with 12% moisture), poultry (0.15 – 117.12 pg/g feed with 12% moisture) and pigs (0.09 – 88.12 pg/g feed with 12% moisture).

The TEQ mean values for the type of feed were 0.09 pg WHO-PCDD/F+dl-PCB-TEQ/g (cattle); 0.02 pg WHO-PCDD/F+dl-PCB-TEQ/g (pigs); respectivly 0.08 pg WHO-PCDD/F+dl-PCB-TEQ/(poultry). Maximul legal limit for compound feed, established in Reg. (CE) No. 574/2011, is 1.5 pg WHO-PCDD/F+dl-PCB-TEQ/g, so we can conclude that from a toxicological point of view these products do not represent a risk for animal health.

3.3. INFLUENCE OF SOME HEAT TREATMENTS USED IN THE PRODUCTION OF COMPOUND FEED ON THE LEVELS OF DIOXINS AND POLYCHLORINATED BIPHENYLS

The sample collection protocol for raw material and compound feed was established in a

way to follow the variation in dioxin and PCB content during two technological processes: drying (used for raw feed material) and granulation (used in the production of compound feed).

3.3.1. Variation of dioxins and PCBs concentration in raw material before and after

the drying process In Table 8 are presented the mean concentrations, standard deviation and ranges

(maximum and minimum) obtained for the PCDD and PCDF congeners in raw material samples before the drying process (input) and after the drying process (output). For the compound OCDF the concentration in green samples (input) was 0.08 pg/g product with 12% moisture, and after the drying the concentration increased to 0.09 pg/g product, in comparation with the congener OCDD for which the concentration before dryin g was 0.53 pg/g product with 12% moisture and it decreased significantly in the dryed product to 0.29 pg/g product with 12% moisture.

Table 8 PCDD and PCDF concentration variation in raw material before and after the drying process

(pg/g product with 12% moisture)

No. Analyzed compound Input Output

Mean SD Min-max Mean SD Min-Max 1. 2,3,7,8-TCDD n.i. n.i. n.i. n.i. n.i. n.i. 2. 1,2,3,7,8-PeCDD n.i. n.i. n.i. n.i. n.i. n.i. 3. 1,2,3,4,7,8-HxCDD n.i. n.i. n.i. n.i. n.i. n.i. 4. 1,2,3,6,7,8-HxCDD n.i. n.i. n.i. n.i. n.i. n.i. 5. 1,2,3,7,8,9-HxCDD n.i. n.i. n.i. n.i. n.i. n.i. 6. 1,2,3,4,6,7,8-HpCDD 0.11 0.03 0.09 – 0.14 0.11 0.05 0.07 – 0.17

XVI

Table 8 (continued)


Mean SD Min-max Mean SD Min-max 7. 1,2,3,4,6,7,8,9-OCDD 0.53 0.37 0.25 – 0.88 0.29 0.16 0.18 – 0.47 8. 2,3,7,8-TCDF n.i. n.i. n.i. 0.04 - n.i. – 0.04 9. 1,2,3,7,8-PeCDF 0.04 0.01 n.i. – 0.05 0.05 - n.i. – 0.05 10. 2,3,4,7,8-PeCDF 0.02 0.01 0.01 – 0.02 0.03 - n.i. – 0.03 11. 1,2,3,4,7,8-HxCDF 0.05 0.00 n.i. – 0.05 0.05 0.02 0.04 – 0.08 12. 1,2,3,6,7,8-HxCDF 0.04 0.00 n.i. – 0.04 0.05 0.03 0.03 – 0.09 13. 1,2,3,7,8,9-HxCDF n.i. n.i. n.i. n.i. n.i. n.i. 14. 2,3,4,6,7,8-HxCDF 0.06 0.01 0.05 – 0.07 0.07 0.03 0.05 – 0.11 15. 1,2,3,4,6,7,8-HpCDF 0.04 0.00 n.i. – 0.04 0.06 0.03 0.04 – 0.10 16. 1,2,3,4,7,8,9-HpCDF n.i. n.i. n.i. n.i. n.i. n.i. 17. 1,2,3,4,6,7,8,9-OCDF 0.08 0.01 0.07 – 0.08 0.09 0.04 0.06 – 0.13 PCDD/F-TEQ measured1) 0.023 0.006 0.02 – 0.03 0.02 0.02 0.01 – 0.05 PCDD/F-TEQ maximum accepted limit2)

0.75

PCDD/F-TEQ action threshold3)

0.5

n.i. – not identified; Input – before drying; Output – after drying PCDD/F-TEQ – Sum of PCDD/F expressed in WHO-TEQ, using WHO-TEF 1998. 1) Values measured in the analyzed samples (pg WHO-PCDD/F-TEQ/g feed with 12% moisture). 2) Maximum content, according Reg. (EC) 547/2011, expressed as pg WHO-PCDD/F-TEQ/g relative to a feedingstuff with a moisture content of 12%. 3)Action thresholt, according Reg. (EC) 547/2011, expressed as pg WHO-PCDD/F-TEQ/g relative to a feedingstuff with a moisture content of 12%.

After calculating the TEQ values the following concentration were obtained 0.023 pg

WHO-PCDD/F-TEQ/g before drying, respectivly 0.02 pg WHO-PCDD/F-TEQ/g after drying. For seven PCDD/F compounds, the concentration in the dryed samples was higher then the concentration in the geen samples.

In Table 9 are presented the mean, standard deviation and ranges (minium and maximum) for PCBs compounds from raw material subjected to the drying process. Of dioxin-like PCBs, PCB 123 was not identified in any sample, and PCB 81 was identified only in one dry sample (0.12 pg/g product). The sum of TEQ values for PCDD/Fs and dioxin-like PCBs did not exceeded the maximum accepted limit of 1.5 pg-TEQ/g product with 12% moisture. For PCB compounds was observed a variation in concentration for 14 PCB congeners, from which the levels for the marker PCBs in the dryed samples were much higher than before drying.

Table 9 PCB concentration variation in raw material before and after the drying process (pg/g product

with 12% moisture)

No. Analyzed compound

Input Output Mean SD Min-max Mean SD Min-Max

1. PCB 28 57.33 41.83 16.40 – 100.0 91.02 65.87 24.30 – 156.0 2. PCB 52 36.70 22.45 14.30 – 59.20 53.10 43.71 21.60 – 103.0 3. PCB 77 1.96 1.46 0.78 – 3.60 2.32 1.34 0.86 – 3.50 4. PCB 81 n.i. n.i. n.i. 0.12 - n.i. – 0.12 5. PCB 101 27.60 16.86 11.0 – 44.70 41.03 41.07 14.0 - 88.30 6. PCB 105 5.33 3.93 1.70 – 9.50 5.82 4.72 2.0 - 11.10 7. PCB 114 0.91 0.42 0.42 – 1.0 1.45 1.51 0.53 – 3.20 8. PCB 118 13.67 9.62 4.40 – 23.60 16.32 15.6 5.50 – 34.20 9. PCB 123 n.i. n.i. n.i. n.i. n.i. n.i. 10. PCB 126 0.18 - n.i. – 0.18 0.14 0.03 0.12 – 0.16 11. PCB 138 20.70 12.59 6.20 –28.90 36.80 42.16 8.90 – 85.30 12. PCB 153 24.83 14.32 8.30 – 33.30 44.32 49.34 11.0 – 101.0 13. PCB 156 1.98 1.14 0.75 – 3.0 3.06 3.42 0.93 – 7.0

XVII

Table 9 (continued)


Input Output Mean SD Min-max Mean SD Min-max

14. PCB 157 0.48 0.35 0.23 – 0.73 0.80 0.70 0.34 – 1.60 15. PCB 167 0.97 0.58 0.31 – 1.4 1.55 1.60 0.48 – 3.40 16. PCB 169 0.19 - n.i. – 0.19 0.19 0.07 0.11 – 0.23 17. PCB 180 10.27 6.30 3.0 – 14.20 16.92 16.25 5.40 – 35.50 18. PCB 189 0.25 0.0 n.i. – 0.25 0.20 0.01 0.19 – 0.21 Sum 6 PCB measured1)

177.43 110.39 59.20 – 277.80 283,18 253,65 85.20 – 569.10

dl-PCB TEQ measured2)

0.01 0.02 0.0 – 0.03 0.02 0.015 0.0 – 0.03

PCDD/F-TEQ measured3)

0.023 0.006 0.02 – 0.03 0.02 0.02 0.01-0.05

Sum PCDD/F+dl-PCB TEQ, maximum accepted limit 4)

1.25

dl-PCB TEQ, action threshold 5)

0.35


3.3.2. Variation of dioxins and PCBs concentration in compound feed before and

after granulation process For the production of granulated compound feed, the grounded and mixed semi-finished

product is subjected to a pelletization (granulation) process, which involves the use of a steam treatment at a temperature of 50 – 80 °C, this helps the pelletisation process. In order to evaluate the effect of the heat treatment during pelletisation process, on the content of persistant organic pollutants, was determined the concent of PCDD/Fs and PCBs from compound feed before and after the pelletisation process.

Table 10 PCDD and PCDF concentration variation in compound feedbefore and after the pelletisation

process (pg/g feed with 12% moisture)


Mean SD Min-max Mean SD Min-Max 1. 2,3,7,8-TCDD 0.04 - n.i. – 0.04 0.10 - n.i. – 0.10 2. 1,2,3,7,8-PeCDD 0.20 - n.i. – 0.20 n.i. n.i. n.i. 3. 1,2,3,4,7,8-HxCDD 0.10 0.06 0.06 – 0.15 0.08 - n.i. – 0.08 4. 1,2,3,6,7,8-HxCDD 0.16 0.19 0.03 – 0.30 0.05 - n.i. – 0.05 5. 1,2,3,7,8,9-HxCDD 0.35 - n.i. – 0.35 0.06 - n.i. – 0.06 6. 1,2,3,4,6,7,8-HpCDD 0.33 0.75 0.04 – 2.80 0.13 0.12 0.05 – 0.51 7. 1,2,3,4,6,7,8,9-OCDD 1.80 3.87 0.07 – 12.90 1.39 3.04 0.06 – 9.90 8. 2,3,7,8-TCDF 0.07 0.01 0.03 – 0.10 0.24 0.33 0.02 – 0.62 9. 1,2,3,7,8-PeCDF 0.05 0.03 0.03 – 0.12 0.11 0.15 0.03 – 0.41 10. 2,3,4,7,8-PeCDF 0.07 0.05 0.04 – 0.13 0.11 0.19 0.02 – 0.50 11. 1,2,3,4,7,8-HxCDF 0.07 0.05 0.02 – 0.18 0.07 0.07 0.01 – 0.26 12. 1,2,3,6,7,8-HxCDF 0.07 0.07 0.03 – 0.25 0.07 0.10 0.02 – 0.32 13. 1,2,3,7,8,9-HxCDF 0.07 0.06 0.02 – 0.11 0.03 0.01 0.02 – 0.05 14. 2,3,4,6,7,8-HxCDF 0.09 0.08 0.04 – 0.34 0.07 0.08 0.03 – 0.34

XVIII

Table 10 (continued)


Mean SD Min-max Mean SD Min-max 15. 1,2,3,4,6,7,8-HpCDF 0.17 0.35 0.02 -1.30 0.10 0.14 0.03 – 0.50 16. 1,2,3,4,7,8,9-HpCDF 0.07 0.08 0.02 – 0.25 0.03 0.01 0.02 – 0.05 17. 1,2,3,4,6,7,8,9-OCDF 0.21 0.41 0.04 – 1.50 0.07 0.03 0.04 – 0.16 PCDD/F-TEQ measured1) 0.057 0.12 0.0 – 0.48 0.04 0.14 0.0 – 0.55 PCDD/F-TEQ maximum accepted limit 2)

0.75

PCDD/F-TEQ action threshold 3) 0.5


Also during the pelletisation process were observed varioations in the concentration for

the PCDD/F compounds, their number was much lower, only for 4 compounds was observed a increased in the granulated product (output) compared with the input. The compounds TCDD, PeCDD and HxCDD were identified only in the samples from unit F6.

The concentration for TCDD increases from 0.04 in the sample before pelletisation, to 0.1 pg/g product with 12% moisture in the final product, while for PeCDD which was identified in the semi-finished sample was not identified in the final product, and the concentration for HxCDD decreases from 0.1 to 0.08 pg/g product with 12% moisture.

Regarding the variation of PCBs content in the analyzed compound feed, can be seen in Table 11, that PCB 123 was not identified in any sample and PCB 81 was identified only in the granulated samples. Comparing the values obtained for bouth situations we can see an increase of PCB concentration in the granulated product (output) but only in the case of four compounds: PCB 28, 52, 77 and 81.

Table 11

PCB concentration variation in feed before and after the granulation process (pg/g feed with 12% moisture)


Input Output Mean SD Min-max Mean SD Min-Max

1. PCB 28 92.24 80.48 14.90 – 268.0 138.13 142.09 16.0 – 515.0 2. PCB 52 84.16 61.03 23.60 – 199.0 93.25 42.83 45.25 – 181.0 3. PCB 77 2.14 1.77 0.55 – 7.40 2.44 1.61 0.72 – 5.80 4. PCB 81 n.i. n.i. n.i. 0.32 0.12 0.24 – 0.41 5. PCB 101 15.77 9.65 7.10 – 42.30 14.98 4.93 7.50 – 24.40 6. PCB 105 3.37 1.98 1.60 – 9.20 3.18 1.25 1.60 – 6.45 7. PCB 114 0.56 0.42 0.23 – 1.80 0.48 0.18 0.25 – 0.88 8. PCB 118 7.03 4.28 3.40 – 19.9 6.40 2.55 3.40 – 13.0 9. PCB 123 n.i. n.i. n.i. n.i. n.i. n.i. 10. PCB 126 0.12 0.05 0.05 – 0.18 0.17 0.21 0.06 – 0.55 11. PCB 138 12.88 12.13 5.20 – 50.3 9.82 3.76 5.30 – 17.40 12. PCB 153 16.03 15.89 6.40 – 67.60 12.57 5.11 7.10 – 22.35 13. PCB 156 0.98 0.88 0.42 – 3.7 0.84 0.34 0.53 – 1.60 14. PCB 157 0.50 0.49 0.18 – 2.0 0.36 0.12 0.19 – 0.57 15. PCB 167 0.55 0.34 0.23 – 1.5 0.45 0.14 0.27 – 0.76 16. PCB 169 0.13 0.03 0.11 – 0.16 0.13 0.05 0.08 – 0.21 17. PCB 180 10.20 13.60 3.60 -55.8 7.55 3.13 4.10 – 14.05 18. PCB 189 0.20 0.19 0.07 – 0.67 0.16 0.07 0.08 – 0.27

XIX

Table 11 (continued)


Input Output Mean SD Min-max Mean SD Min-max

Sum 6 PCB measured1)

225.94 96.65 79.4 – 363.10 244.87 86.01 126.5 – 688.30

dl-PCB TEQ measured2) 0.004 0.007 0.0 – 0.02 0.007 0.017 0.0 – 0.06

PCDD/F-TEQ measured3)

0.057 0.12 0.0 – 0.48 0.04 0.14 0.0 – 0.55

PCDD/F + dl-PCB TEQ, maximum accepted limit 4)

1.5

dl-PCB TEQ, action threshold 5)

0.5

n.i. – not identified; Input – before drying; Output – after drying PCDD/F-TEQ – Sum of PCDD/F expressed in WHO-TEQ, using WHO-TEF 1998. 1) Values measured in the analyzed samples (pg WHO-PCDD/F-TEQ/g feed with 12% moisture). 2) Maximum content, according Reg. (EC) 547/2011, expressed as pg WHO-PCDD/F-TEQ/g relative to a feedingstuff

with a moisture content of 12%. 3)Action thresholt, according Reg. (EC) 547/2011, expressed as pg WHO-PCDD/F-TEQ/g relative to a feedingstuff with a

moisture content of 12%.

Regarding the contaminant content in the feed raw material, there were no significant

variations for dioxins, but for the PCB content were observed significant variation between the before and after drying samples. The results of the <Paried t-Test> were the following: P < 0.05; S *. Regarding the content of dioxins and PCBs, applying the <Paried t-Test> shows that there are no significant variations between the samples analyzed before and after the pelletisation process (P>0.05).

4. TRACEABILITY STUDY OF POLYCHLORINATED BIPHENYLS IN AGRI-FOOD

CHAIN

Results obtained in this chapter are meant to ilustate the way in which the concentration of one compound varies along the food chain. The selected food chain is compoused of soil – vegetation – milk, and the contaminats analyzed in traceability were non-dioxin-like PCBs (PCB 28, 52, 101, 138, 153, 180) and dioxin-like PCBs (PCB 77, 105, 114, 118, 123, 126, 156, 157, 167, 169, 189). The soil and vegetation were selected as environment matrix were takes place the accumulation of persistant organic pollutants like PCBs, and the milk (cows and sheep milk) was selected as the food matrix that reflects the contamination of a selected georaphycal area.

4.1. INCIDENCE OF POLYCHLORINATED BIPHENYLS IN SOIL SAMPLES

In order to establish the incidence of polychlorinated biphenyls (PCB) in soil samples

were proposed the following steps: collection of soil samples from different depths (0 – 5 cm and 5 – 10 cm); determination of dry matter and water contentfrom samples; identification and quantification of dioxin-like and non dioxin-like polychlorinated biphenyls (PCBs) by gas-chromatography (GC-ECD).

4.1.1. Water content determination from soil samples The data regarding the mean water and dry matter from soil samples are presented in

Table 12, each sample was done in duplicate.

XX

Table 12 Moisture and dry substances content (mean value, %) from soil

No. Collection area

0 – 5 cm 5 – 10 cm Moisture Dry matter Moisture Dry matter

1. S1HoP 7.39 92.61 7.71 92.29 2. S2MbF 7.50 92.50 6.72 93.28 3. S2MbP 4.18 95.82 4.05 95.95 4. S3LbF* 6.85 93.15 7.93 92.07 5. S3LbP* 11.53 88.47 16.47 83.53 6. S3LoP1* 3.99 96.01 3.87 96.13 7. S3LoF1* 4.38 95.62 4.34 95.66

* From: CHELEMAN et al., 2011a Water and dry matter content was determined in the same day when the extraction of

PCBs compounds from soil samples was done, because the results regarding the soil contamination are expressed on a dry matter base.

4.1.2. Identification and pattern of polychlorinated biphenyls in soil The chart from figure 7 presents the pattern distribution of PCBs from soil samples

collected at two depths: 0-5 cm and 5-10 cm. Values are expressed in ng/g dw, and the limits from 10 ng/g dw represints the alert level for sensitive areas, and the one at 40 ng/g dw represints the alert level for less sensitive areas (see Table 14).

PC

B 2

8

PC

B 5

2

PC

B 7

7

PC

B 1

01

PC

B 1

05

PC

B 1

14

PC

B 1

18

PC

B 1

23

PC

B 1

26

PC

B 1

38

PC

B 1

53

PC

B 1

56

PC

B 1

57

PC

B 1

67

PC

B 1

69

PC

B 1

80

PC

B 1

890

5

10

15

20

25

30

35

40

0 - 5 cm

5 - 10 cm

ng

/g s

.u.

Fig. 7. PCBs compound distribution in soil samples from different depths 0-5 cm and 5-10

cm As we can observ from the chart in samples collected from a depth of 0-5 cm were

identified all 17 PCBs compounds, while in samples collected from 5-10 cm PCB 189 was not identified. There were differences between the concentration and distribution of PCBs in soil samples from different depths. Comparing with the other analyzed compounds, PCB 138 was the one with the higher concentration having a mean of 37.87 ng/g dw for samples from 0-5 cm and 6.29 ng/g dw for samples from 5-10 cm.

XXI

4.1.3. Variation of PCBs content in soil samples from the studied georaphical areas Table 13 shows the variation of six marker PCBs, respectively dioxin-like PCBs from

soil samples collected from three areas S1H, S2M and S3L. Table 14 presents the Romanian refferences values for six marker PCB and one dioxin-like PCB (PCB 118). For the area S1H the values represent the concentration and not the mean were the compound was identified only in one sample.

Table 13 Variation PCB level in soil samples collected from areas S1H, S2M and S3L (ng/g dw)

Compound S1H S2M S3L Mean ±±±± SD Frequency

(n = 2) Mean ±±±± SD Frequency


(n =8) PCB 28 0.18 1 0.18 ± 0.07 4 0.21 ± 0.17 8 PCB 52 n.i. - 0.22 ± 0.21 3 0.32 ± 0.38 6 PCB 77 0.31 1 0.17 1 1.37 ± 1.33 3 PCB 101 0.19 1 0.74 ± 1.04 3 0.16 ± 0.13 8 PCB 105 0.04 1 0.18 ± 0.08 3 1.32 ± 1.05 3 PCB 114 0.005 1 n.i. - 0.31 ±0.36 7 PCB 118 1.18 ± 1.43 2 0.31 ± 0.17 4 0.44 ± 0.43 6 PCB 123 n.i. - n.i. - 0.68 ± 0.22 2 PCB 126 n.i. - n.i. - 1.25 ± 0.80 4 PCB 138 38.45 ±

54.0 2 52.27 ± 87.83 4 1.76 ± 2.23 6

PCB 153 0.15 ± 0.19 2 0.09 ± 0.03 3 1.20 ± 0.86 3 PCB 156 n.i. - n.i. - 1.06 ± 0.87 3 PCB 157 0.51 ± 0.03 2 0.75 ± 0.80 4 1.35 ± 0.83 3 PCB 167 n.i. - n.i. - 0.68 ± 0.20 3 PCB 169 n.i. - n.i. - 0.53 ± 0.43 3 PCB 180 0.36 1 0.54 ± 0.03 2 0.48 ± 0.34 4 PCB 189 0.36 1 n.i. - 0.05 1 Sum PCB 39.33 54.04 4.13 Sum dl-PCB

2.40 1.41 9.04

WHO-TEQ

0.0 0.0 0.06

S1H, S2M, S3L – selected area for soil sample collection. n.i. – not identified n – total number of samples collected from one area WHO-TEQ – Dioxin-like PCBs concentration expressed as toxic equivalence (TEQ) calculated using the toxic equivalence factors (TEF) established by the World Health Organization (WHO) in 1998.

From the table above we can see that compound PCB 138 had much higher

concentrations in samples from area S1H (38.45 ng/g dw) and S2M (52.27 ng/g dw) compared with area S3L (1.76 ng/g dw). Comparing the sum of six PCBs compounds with the references values (Table 14), it can be seen that area S3L had a concentration under 10 ng/g dw, so it was in the legal limits, while area S1H, respectivly S2M both had concentrations over the normal limits, but did not exceed the action threshold for sensitive areas (250 ng/g dw). Although initially area S1H it was supposed to have a higher contamination level, followed by S2M and S3L, according to the data from the above table we can conclude that from the point of six marker PCBs (sum), area S2M had a higher concentration, fllowed by S1H and S3L.

Regarding dioxin-like PCBs, in the samples from area S3L were identified all 11 PCB compounds PCB, in samples from S1H were not identified PCB 123, 126, 156, 167 and 169,

XXII

and in samples from S2M were not identified PCB 114, 123, 126, 156, 167, 169 and 189. Comparing the sum of dioxin-like PCBs there is a change in the ranking of areas S3L is on the first place with a concentration of 9.04 ng/g dw, followed by area S1H with a concentration of 2.4 ng/g dw and finally S2M with a concentration of 1.41 ng/g dw.

Table 14 Valori de referin ţă (ng/g dw) for pragurile de alertă and intervenţie privind compuandi

PCB din soil

Compound Normal values

Alert levels Intervention levels Sensitive Less sensitive Sensitive Less sensitive

PCB 28 <0.1 2 10 10 50 PCB 52 <0.1 2 10 10 50 PCB 101 <0.4 10 40 40 200 PCB 118 <0.4 10 40 40 200 PCB 138 <0.4 10 40 40 200 PCB 153 <0.4 10 40 40 200 PCB 180 <0.4 10 40 40 200 Total PCB <10 250 1000 1000 5000

Alert level - determines a supplementary monitoring and reduction of these pollutants Intervention level – determines actions to be taken (soil cleaning, restricted access, etc) Sensitive: residential and agricultural areas, playing fields, parks, protected areas, sanitary zones under restriction regime Less sensitive: industrial and commercial land uses From: Covaci et al. 2003.

Applying the statistic test <One-Way ANOVA> for water content from soil samples

collected at different depths shows that there are no significant variations between samples collected from 0-5 cm and samples collected from 5-10 cm (P>0.05), but there were distinct significant variations between collection areas, the results were the following: F=13.15; R2 = 0.9185; P<0.01; S **. Regarding the PCBs content from soil, by applying the test <One-Way ANOVA> there were not found statistic significances between the PCB content detected in samples collected from the three areas (S1H, S2M and S3L).

4.2. INCIDENCE OF POLYCHLORINATED BIPHENYLS IN VEGETATION

To establish the incidence of PCBs contamination in vegetation samples were proposed

the following activities: determination of water and dry matter content, expressed in percentage; identification and quantification of dioxin-like PCBs compounds and six marker PCBs from vegetation samples collected at the same area as the soil samples.

4.2.1. Moisture content determination from vegetation samples Table 15 prezents the mean values for the water and dry matter content in vegetation

samples collected from the same locations as the soil samples. Each sample was determined in duplicate.

The vegetation samples are represente by the grass that grows in these areas and is used as grazing pastrure for the livestock. Locations S1HoP, S2MbF, S3LbF, S3LbP and S3LoP1 are land used for the extensive grazing, while locations S2MbP and S3LoP2 are natural pastures used to obtaine green feed.

XXIII

Table 15 Moisture and dry substances content (mean value, %) from vegetation samples

No. Collection area Moisture Dry matter

1 S1HoP 10.07 89.93

2 S2MbF 8.70 91.30

3 S2MbP 10.23 89.77

4 S3LbF 13.15 86.85

5 S3LbP 11.67 88.33

6 S3LoP1 11.87 88.13

7 S3LoP2 11.68 88.32 4.2.2. Identification and pattern of polychlorinated biphenyls in vegetation samples Figure 8 represints the pattern distribution of dioxin-like PCBs (PCB 77, PCB 105, PCB

114, PCB 118, PCB 123, PCB 126, PCB 156, PCB 157, PCB 167, PCB 169, PCB 189) and non-dioxin-like PCBs (PCB 28, PCB 52, PCB 101, PCB 138, PCB 153 and PCB 180) dfrom vegetation samples.

Fig. 8. PCBs compound distribution in vegetation samples (ng/g dw)

It can be seen in the chart that from the group of dioxin-like PCBs were identified only

PCB 105, PCB 118 and PCB 157, and from the non-dioxin-like PCBs were identified all the six compounds. Comparing with the distribution of PCB conpounds from soil samples, the distribution patterns are different, because in soil the major compound was PCB 138, while in vegetation samples the major compound is PCB 180.

4.2.3. Variation of PCBs content in vegetation samples from different areas In table 16 is presented the content for dioxin-like and non-dioxin-like PCBs from

vegetation samples, depending from the collection area. For area S1H were analyzed only one set of samples because soil samples were collected only from one location (S1HoP).

XXIV

Table 16 Variation of PCB levels in vegetation samples collected from areas S1H, S2M and S3L

(ng/g dw) Compound S1H S2M S3L

Concentration (n = 1)

Concentration Frequency (n = 2)

Concentration Frequency (n =4)

PCB 28 0.41 0.11 1 0.16 ± 0.06 4 PCB 52 0.51 0.65 ± 0.33 2 0.45 ± 0.45 2 PCB 77 n.i. n.i. - n.i. - PCB 101 6.25 0.97 ± 0.08 2 0.67 ± 0.42 3 PCB 105 0.92 1.00 1 0.43 ± 0.35 2 PCB 114 n.i. n.i. - n.i. - PCB 118 0.41 n.i. - 0.52 1 PCB 123 n.i. n.i. - n.i. - PCB 126 n.i. n.i. - n.i. - PCB 138 0.32 6.15 ± 1.37 2 8.98 ± 12.50 3 PCB 153 0.19 0.56 1 0.15 1 PCB 156 n.i. n.i. - n.i. - PCB 157 8.25 n.i. - 0.05 1 PCB 167 n.i. n.i. - n.i. - PCB 169 n.i. n.i. - n.i. - PCB 180 6.95 12.34 ± 1.80 2 4.96 ± 4.84 4 PCB 189 n.i. n.i. - n.i. - Sum PCB 14.63 20.78 15.37

Sum dl-PCB

9.58 1.00 1.00

WHO-TEQ 0.0 0.0 0.004 S1H, S2M, S3L – selected area for soil sample collection. n.i. – not identified n – total number of samples collected from one area OMS-TEQ – Dioxin-like PCBs concentration expressed as toxic equivalence (TEQ) calculated using the toxic equivalence factors (TEF) established by the World Health Organization (WHO) in 1998.

Regarding the sum of six PCB compounds it can be seen that in this situation collection

area S2M has a higher concentration (20.78 ng/g dw) followed by area S3L with a concentration of 15.37 ng/g dw, and area S1H with a concentration of 14.63 ng/g dw. From this point of view it can be concluded that area S2M has a higher contamination level, same as for the non-dioxin-like PCBs content measured in soil.

From a total of 11 PCB compounds analsyzed for area S1H and S3L were identified only three compounds (PCB 105, PCB 118, PCB 157), and for the samples from area S2M only compound PCB 105 had a concentration above the method detection limit (<0.005 ng/g). Analysing the situation of areas in terms of total concentration of dioxin-like PCB compounds is observed that in this case the S1H (originally assigned to a high contamination level) had a higher degree of contamination, 9.58 ng / g dw, compared to the other two areas which both had a concentration of 1.0 ng / g dw. Regarding the TEQ values for the dioxin-like compounds, it is seen that this were 0 for S1H and S1M, and 0.004 ng-TEQ/g dw for S3L.

From the area S3L were also collected hay samples from different mowing periods: S3Lb Fp – fresh mowed hay; S3Lb F1s – one week hay and S3Lb FU – dry hay one year old (Table 17).

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Table 17 Variation of PCBs levels in hay samples collected from area S3L at different periods (ng/g dw)

Compound Sample S3Lb Fp S3Lb F1s S3Lb Fp

PCB 28 0.22 0.34 0.34 PCB 52 0.19 0.32 0.19 PCB 77 n.i. n.i. n.i. PCB 101 1.01 0.77 0.82 PCB 105 0.20 n.i. n.i. PCB 114 n.i. n.i. n.i. PCB 118 0.09 0.09 n.i. PCB 123 n.i. n.i. n.i. PCB 126 n.i. n.i. n.i. PCB 138 n.i. 1.82 n.i. PCB 153 n.i. n.i. n.i. PCB 156 n.i. n.i. n.i. PCB 157 1.03 0.82 n.i. PCB 167 n.i. n.i. n.i. PCB 169 n.i. n.i. n.i. PCB 180 n.i. n.i. 0.11 PCB 189 n.i. n.i. n.i. Sum 6 PCB 1.48 3.24 1.46 Sum dl-PCB 1.33 0.92 0 WHO-TEQ 0.001 0 0

WHO-TEQ – Dioxin-like PCBs concentration expressed as toxic equivalence (TEQ) calculated using the toxic equivalence factors (TEF) established by the World Health Organization (WHO) in 1998.

In the fresh hay (S3Lb Fp) from six non-dioxin-like PCBs were identified only three

compounds (PCB 28, PCB 52, PCB 101), and from 11 dioxin-like PCBs were identified only PCB 105, PCB 118 and PCB 157. The TEQ value for the dioxin-like PCBs, calculated with the help of the toxic equivalence factors (TEF), was 0.001 ng-TEQ/g dw. The one week old hay (S3Lb F1s) presented a higher contamination, so from a total of 17 analyzed compounds were identified four non-dioxin-like PCBs (PCB 28, PCB 52, PCB 101, PCB 138) and two dioxin-like PCBs (PCB 118, PCB 157). The TEQ value for this sample was 0. In the case of one year old hay (S3Lb FU) the situation was different, so from a total of 17 analyzed compounds were identified only four compounds PCB 28, 52, 101, 180, all belonging to the non-dioxin-like PCBs.

Regarding the dry matter content in vegetation samples collected from different areas, applying the test <One–Way ANOVA> were found an extremely significant correlations for this parameters in samples collected from different areas (F=186.9; R2=0.9938; P<0.0001; S***). For the hay samples collected from S3L was used the statistic test <One–Way ANOVA> and significant variations were found for the moisture content (F=967.7; R2=0.9985; P<0.0001; S***) and also for the dry matter (F=2272.9; R2=0.9993; P<0.0001; S***). Applyng the test <One–Way ANOVA> for the content of PCBs in vegetation samples no significant variations were found between samples collected from the three areas S1H, S2M, respectivly S3L (P>0.05). The same situation was found for the hay samples from S3L.

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4.3. INCIDENCE OF POLYCHLORINATED BIPHENYLS IN MILK In terms of contamination with persistant organic pollutants, milk represents, in our

research, the last link in the food chain. Because milk in an essential food product with a wide used, in the study regarding milk contamination, was imposed, as necessary, the quality parameter evaluation. For the evaluation of milk quality and establish the incidence of PCBs in milk were propoused the following activities: evaluation of milk samples quality by analyzing some phisico-chemical parameters; identification and quantification of dioxin-like and non-dioxin-like PCBs from milk samples.

4.3.1. Determination of some physicochemical parameters in milk samples using the

LactoStar analyzer Table 18 presents the physic-chemical parameters determined in two cow milk samples:

one before pasteurization (whole milk) and after pasteurization (normalized milk). Table 18

Quality parameters determined in whole and standardized cow milk Parameter Whole milk STAS

2418/98 Normalizate milk STAS

143/84 Mean DS Mean DS

Water, % 88.06 0.03 91.05 0.02

Total dry matter, %

11.94 0.03 8.95 0.02

Fat, % 3.46 0.01 3.2 1.22 0.03 1.5

Protein, % 3.28 0.01 3.2 2.97 0.01 3.2

Lactose, % 4.54 0.01 4.18 0.05

DWN, % 8.48 0.02 8.5 7.73 0.04 8.5

Acidity, °°°°T 18.83 0.29 15…19 15.20 0.61 15…21

During the extraction of PCBs content from milk, the fat content was also determined

using a gravimetris method.

Seara / Night Dimineata / Morning0.0

2.5

5.0

7.5

10.0S1H

S2M

S3L

Co

ntin

ut

de

gra

sim

e / F

at c

on

ten

t(%

)

Fig. 9. Fat content (%) variation in milk samples collected from locations S1H, S2M and S3L

It can be seen that in the first two collection areas, milk fat content tends to decreas in

the morning samples (8.58% for S1H and 1.18% for S2M) compared with the evening (9.56% for S1H and 3.74% for S2M). In exchange for area S3L the thrend is reversed, and so the fat content in evening samples was lower 5.38%, compared with that from morning samples,

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6.98%. the big differences that can be seen between S1H and S2M is explaned by the fact that the milk samples from S1H were of sheep milk, while the one collected from S2M were cows milk.

4.3.2. Identification and pattern of polychlorinated biphenyls in milk samples Figure 10 presents the pattern distribution of PCB compounds (dioxin-like and non-

dioxin-like PCBs) from milk samples. Seen from the figure, the dioxin-like PCBs 77, 114, 123, 126, 156, 167, 169 and 180 were not identified in any sample, while the non-dioxin-like compounds were identified in all analyzed milk samples. PCB 180 is the major compound (0.29 – 34.82 ng/g fat) followed by PCB 138 (0.18 – 25.56 ng/g fat) and PCB 157 (2.93 ng/g fat).

Fig. 10. PCBs compound distribution in milk samples (ng/g fat)

This distribution pattern of PCB compounds is almost identical with the one from the

vegetation samples, except for the ranking of PCB 180 (first), followed by PCB 138 and PCB 157.

4.3.3. Variation of PCBs content in milk samples from different areas Table 19 presents the concentration of non-dioxin-like PCBs, respectively dioxin-like

PCBs from analyzed samples milk. Table 19

Variation of PCB content in milk samples collected from areas S1H, S2M and S3L (ng/g fat)

Compound S1H S2M S3L Mean ±±±± SD Frequency



(n =4) PCB 28 0.53 ± 0.11 2 0.28 1 0.57 ± 0.19 4 PCB 52 n.i. - n.i. - 2.13 1 PCB 77 n.i. - n.i. - n.i. - PCB 101 0.11 ± 0.02 2 0.51 ± 0.18 2 0.58 ± 0.32 3 PCB 105 0.89 ± 0.47 2 3.79 ± 3.13 2 2.17 ± 2.36 2 PCB 114 n.i. - n.i. - n.i. - PCB 118 0.09 1 0.46 1 0.36 1 PCB 123 n.i. - n.i. - n.i. - PCB 126 n.i. - n.i. - n.i. - PCB 138 0.52 ± 0.12 2 12.97 ± 17.82 2 3.50 ± 3.72 4 PCB 153 1.76 ± 0.06 2 2.24 ± 3.11 2 0.91 ± 0.87 2 PCB 156 n.i. - n.i. - n.i. - PCB 157 n.i. - n.i. - 2.93 1 PCB 167 n.i. - n.i. - n.i. - PCB 169 n.i. - n.i. - n.i. -

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Table 19 (continued) Compus S1H S2M S3L

Media ±±±± DS Frecvenţa (n = 2)

Media ±±±± DS Frecvenţa (n = 2)

Media ±±±± DS Frecvenţa (n =4)

PCB 180 0.55 ± 0.001 2 34.82 1 0.36 ± 0.40 3 PCB 189 n.i. - n.i. - n.i. - Sum PCB 3.47 50.82 8.05 Sum dl-PCB

0.98 4.25 5.46

WHO-TEQ

0.0 0.0 0.0

S1H, S2M, S3L – area code for milk sample collection n.i. – not identified n – total number of samples collected from one area WHO-TEQ – Dioxin-like PCBs concentration expressed as toxic equivalence (TEQ) calculated using the toxic equivalence factors (TEF) established by the World Health Organization (WHO) in 1998.

Comparing with the soil samples, in milk samples from the sic marker PCBs, compound

PCB 52 was not ifentified in area S1H, the same situation for the milk samples from area S2M, and for S3L only one sample was pozitive. The legislative organization from the European Union, have not yet established maximum limits for non-dioxin-like PCBs, but compared with the proposed limit (25 ng/g fat), milk samples collected from area S2M have 2 times higher concentrations. The sample that exceeded the propoused limit was a pasteurized cow milk samples collected from S2M (65.45 ng/g fat), being much higher than the concentration found by de BAYAT et al. (2011) in pasteurized milk samples.

Area ranking in thersm of total non-dioxin-like PCBs content is: S2M – high contamination (50.82 ng/g fat), followed by S3L – medium contamination (8.05 ng/g fat), and S1H – reduced contamination (3.47 ng/g fat). Regarding the dioxin-like PCBs, it can be seen from Table 19 that from 11 analyzed compounds, for samples from S1H and S2M were detected only two cmpounds PCB 105 and PCB 118, and for the samples from area S3L, three compounds PCB 105, PCB 118 and PCB 157. The ranking of the areas according to dioxin-like PCBs content is: S3L cu 5.46 ng/g fat, S2M cu 4.25 ng/g fat and S1H cu 0.98 ng/g fat.

Applying the test <One–Way ANOVA> for the fat content in milk samples there were no significant variation between the samples collected in the evening and those collected in the morning (P>0.05), but there were significant variations according to the sampling area (F=13.09; R2=0.8972; P<0.05; S*). Regarding the physic-chemical parameters in non-pasteurized and pasteurized milk, there are no significant variations (P>0.05). For the content of PCBs in milk samples was applied the test <One–Way ANOVA> and were found distinct significance, with the following results: F=4.158; R2=0.4470; P>0.001; S **.

5. CORRELATION OF SOME ANALYZED PARAMETERS WITH DIO XIN AND

POLYCHLORINATED BIPHENYLS

In order to explain how dioxin and PCB content is influenced by some analyzed physic-chemical parameters we proceeded to the simple liniar regression analysis, and to measure the intensity of the connetction between variables we calculated the correlation coeficient (r) which has the values between -1 and +1. The determination coefficient (R2) with values between 0 and 1. The higher the coefficient of determination is, the stronger is the conection between variable.

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5.1. CORRELATION OF SOME PARAMETERS WITH DIOXIN AND POLYCHLORINATED BIPHENYLS CONTENT IN ANALYZED FEED SAMPLES

For the feed samples correlations were made between water and dry matter, and total

dioxins and PCBs content in raw feed materials and compound feed materials. Figure 11 presents the corelogram between dioxin content (PCDD/Fs compounds) and water (A) and dtry matter (B) form raw feed materials. The realationship in figure 11 A is represented by a regression line with a decreasing trend, which means a revers linear conection between water and dioxin content (R2 = 0.79). The situation for dry matter and dioxin content is reversed (Fig. 11 B) so the relationship is described by a upward regression line, indicating a direct linear connection between the analyzed variables (R2 = 0.78).

A B

Fig. 11. Correlation between moisture (%) – A, dry matter (%) - B and dioxin content (sum of 17 PCDD/F compounds, pg/g product) determinate from raw feed material samples

A B

Fig. 12. Correlation between moisture (%) – A, dry matter (%) - B and PCB content (sum of 18 PCBs compounds, pg/g product) determinate from raw feed material samples

In figure 12 is presented the correlation between PCB content and moisture (A) and dry

matter (B). For measuring the intensity between these variables was calculated the linear correlation coefficient which in both cases (r = -0.25, r = 0.25) indicated a very wweak realtion between rhese variables.

The corelogram from figure 13 presents not significant correlations between dioxin content (PCDD/Fs) and moisture (r = 0.06), substanță uscată (r = 0.07).

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A B

Fig. 13. Correlation between moisture (%) – A, dry matter (%) - B and dioxin content (sum of 17 PCDD/F, pg/g product) analyzed in compound feed

Figure 14 presents the correlation between PCB content and moisture, dry matter

determined in compound feed. Analyzeing the data with the regression equiation, we can state that there were no significant correlations between PCB content and moisture (r = 0.22), respectivly dry matter (r = -0.23).

A B

Fig. 14. Correlation between moisture (%) – A, dry matter (%) - B and PCBs content (sum of 18 PCB compounds, pg/g product) determinate in compound feed

Analyzing the correlation between dioxin, PCBs and fat content, using the linear

regression (Fig. 15), for three category of feed (cattle, pigs, poultry) the following results were obtained: between dioxins and fat content no significant correlations were found (r = -0.37), but between PCB content and fat a high linear correlation was found.

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A B

Fig. 15. Correlation between fat content (%) and dioxine content (sum of 17 PCDD/F, pg/g product) – A, PCBs content – B (sum of 18 PCB compounds, pg/g product) determinate in

compound feed

5.2. CORRELATION OF SOME PARAMETERS AND POLYCHLORINATED BIPHENYLS IN ANALYZED ENVIRONMENTAL SAMPLES

For the soil samples linear regression was analyzed in both types of samples: collected at

0 – 5 cm, and 5 – 10 cm. In figure 16 are presented the correlations between PCB content detercted in soil

samples from 0 – 5 cm and moisture (Fig. 16 A), dry matter (Fig. 16 B). Analyzend the data with the regression equation not significant correlations were found between PCBs content and moisture (r = -0.36) and dry matter (r = 0.36).

A B

Fig. 16. Correlation between moisture (%) – A, dry matter (%) – B and PCB content (sum of 17 PCB compounds, ng/g dw) determinated in soil samples collected from a depth of 0 – 5 cm

The determination coefficient was identical (R2 = 0.26) in both cases, indicating that for

the analyzed samples the variation of PCB content is 26% due to the variation of moisture and dry matter. For measuring the intensity of the relationship between the variables was calculated the linear correlation coefficient (r) indicating in both cases a moderate negative correlation (moisture) and positive correlation for dry matter (Fig. 17).

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A B

Fig. 17. Correlation between moisture (%) – A, dry matter (%) – B and PCB content (sum of 17 PCB compounds, ng/g dw) determinate in soil samples collected from a depth of 5 – 10 cm

In the next corelograms are presented the correlation of vegetation samples grouped in

grass (collected from the same locations as the soil samples) and hay samples collected from area S3L. In figure 18, the determination coefficient (R2 = 0.21) shows that for the grass samples the variation of PCB content is explained in a proportion of 21% by the variation of moisture, and the linear correlation coefficient (r = -0.46) indicates a lowe linear correlation between this variables. The linear correlation coefficient (r = 0.59), for the hay samples indicates a moderate linear correlation between the two variables.

A B

Fig. 18. Correlation between moisture (%) and PCB content (sum of 17 PCB compounds, ng/g dw) determinated in vegetation samples (A – grass, B - hay)

Figure 19 presents the correlation between PCB content and dry matter from grass (A)

and hay (B). Determination coefficient (R2 = 0.21) shows that for the vegetation samples the PCB content is explained only 21% by the variation of dry matter, and so the relationship between the two variables has a lower intensiti (r = 0.46). For the hay samples the linear correlation coefficient (r = 0.60) indicates a medium conections between PCB content and dry matter.

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A B

Fig. 19. Correlation between dry matter (%) and PCBs content (sum of 17 PCB, ng/g dw) determinated in vegetation samples (A – grass, B - hay)

5.3. CORRELATION OF SOME PARAMETERS AND POLYCHLORINATED BIPHENYLS IN ANALYZED MILK SAMPLES

In order to explaine to what extent the variation of PCB content (dioxin-like and non-

dioxin-like compounds) is influenced by the fat content determined in the milk samples, was used simple linear regression analysis, and for measuring the intensity of the relationship between the two variables was calculated the linear correlation coeficient (r). Linear regression analysis was determined on cow milk and sheep milk.

A B

Fig. 20. Correlation between fat content (%) and PCBs content (sum of 17 PCB, ng/g fat) determinated in cow milk samples (A) and sheep milk (B).

The linear correlation coefficient (r = -0.80) indicates a strong relationship between PCB

and fat content (Fig. 20.A). The determination coefficient (R2 = 0.53) shows in the case of four sheep milk samples that the PCB variation is explained in a proportion of 53% by the variation of milk fat content. The linear correlation coefficient (r = 0.73), indicates a high intensity correlation between PCB content and crude fat determined in sheep milk.

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6. GENERAL CONCLUSIONS 1. The first objective was to assess feed quality by determining the physico-

chemical parameters and to identifphy the levels of dioxins and PCBs. Results showed that all analyzed parameters were the same as those specified in the literature, and in the feed samples were identified all the contaminants, exception TCDD, PeCDD and HxCDD which were not identified in the raw material samples, but were identified in the compound feed.

• Regarding the drying process applied for the raw material from a total of 17 PCDD/F compounds analyzed, at seven compounds the concentration increased in the samples analyzed after the drying process, compared with those analyzed before the drying process. Regarding PCB compounds, froma unumber of 18 compounds analyzed at 14 was seen an increase in the dried samples. Even so, the concentrations measured in the final products did not exceed the accepted maximum limits.

• Regarding the influence of the granulation process applied for the compound feed, the concentration in the final product (feed pellets) increased only at four out of 17 PCDD/F compounds PCDD/F, respectivly only at four out of 18 PCB compounds (PCB 28, PCB 52, PCB 77 and PCB 81). The differences in concentration of these compounds before and after the granulation process are not so high, and an explination woulb be that there was a secondary contamination of these products due to the steam used or during the cooling process.

• From a toxicological point of view no sample raise any problems because the samples for both raw material and compound feed the toxic equivalence - TEQ values (PCDD/F+dioxin-like PCBs) were under the accepted maximum limits: 1.25 pgTEQ/g product for raw material and 1.5 pg-TEQ/g feed for compound feed.

• Contaring the compound feed production factoryes, unit F6 had a content of PCDD/Fs higher as the rest of the units (0.01 – 0.55 pg PCDD/F-TEQ/g feed with 12% moisture) becous there were identified the TCDD, PeCDD and HxCDD compounds in those sample, compounds that were not identified in the other samples.

• The mean TEQ values (PCDD/F+PCB) for each category of feed were way bellow the maximum limits, so for the cattle feed the concentration was 0.09 pg TEQ/g feed, for poultry feed was 0.07 pg TEQ/g feed, and for pigs feed the TEQ concentration was 0.03 pg/g feed. These results are comparable with the ones reported by LORBER et al. in 2004. 2. The second objective aimed to study the incidence of persistent organic pollutants in environmental samples. For this objective were analyzed dioxin-like polychlorinated biphenyls and marker PCB from soil and vegetation samples. The pattern distribution of PCB compounds from soil and vegetation samples was identical with the one reported in the studied literature.

• In the samples collected from area S3L were identified all the 17 PCB compounds, for area S1H were identified only 11 compounds, and for area S2M only 10 PCB compounds. The sum of PCBs in the studied areas was 2.37 ng/g dw for S3Lb and 10.4 ng/g dw for S3Lo, which were identical with concentrations found in other rural areas from Romania (COVACI et. al., 2003, DRĂGAN et. al, 2006).

• The sum of 17 PCBs in surface soil (0 - 5 cm) varied between între 0.68 - 16.75 ng/g dw, while for the depth of 5 - 10 cm concentrations varied between 0.41 - 22.29 ng/g dw. This decline in PCB concentration at different depths in nom-agricultural areas was also, reported by ARMITAGE et. al. (2006) that studied the level and vertical distribution of PCBs în agricultural and natural soils from Sweden.

• Regarding non-dioxin-like PCBs concentration in soil samples the alert and intervention levels for sensitive areas were not exceeded, but for some compounds the normal values were exceeded.

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• Dioxin-like compounds were identified in all the vegetation samples collected from the same locations as the soil samples. In samples from S1H and S2M were identified only three dioxin-like PCBs (PCB 105, PCB 118, PCB 157), and in samples from S2M only one compound (PCB 105). Because of their semivolatile properties it can be seen that for the hay sample older than one year the concentration decreased significantly.

• In terms of total PCB content from vegetation samples the ranking of the selected areas is the same, so: S1H – highly contaminated (24.21 ng/g dw); S2M – medium contamination (21.78 ng/g dw) and S3L – low contamination (16.37 ng/g dw). 3. Regarding the physico-chemical parameters of milk samples was observed that for the whole milk all the parameters were within the Romanian legal limits, while for the normalized milk these were under the set limints.

• The pattern distribution of the investigated contaminants in milk samples was identical with the distribution found in the vegetation samples, the same number and type of compounds were identified for both type of samples. The concent of non-dioxin-like PCBs (sum of PCB 28, 52, 101, 138, 153, 180) in samples from S2M exceeded the propused maximum limit (25 ng/g fat).

• The classification of the selected area in terms of total PCBs is: S2M – high degree of contamination with a concentratio of 55.07 ng/g fat; S3L – medium contamination with a concentration of 13.51 ng/g fat and S1H – low degree of contamination with a concentration of 4.45 ng/g fat.

• The research in the present study indicates that PCB levels are higher in milk samples from privat farms, fact explaind by the contamination of soil and feed with these compounds. The prevention and reduction of these contaminants can be achived by monitoring the levels of PCBs in the environment and feed. This is where the Good Practices guidelines and the HACCP system for the milk processing units step in (TOFANĂ and SOCACI, 2008; CHELEMAN et al., 2009). 4. Regarding the correlation of contaminant level and water and dry matter analyzed in the raw feed materials was found a revers negative correlation (for moisture content) and a positive direct correlation for the dry matter. The variation of dioxin content (PCDD/F) in the raw feed material is explained in 79% by the variation of water and dry matter.

• Regarding the correlation of PCBs content from raw feed materials, dioxin and PCBs content from compound feed, with moisture and dry matter no significant variations were observed. The variation of PCBs content from compound feed can be explaind in a propotion of 98% by the variation of crude fat content.

• For the soil sample collected from a depth of 0 – 5 cm were found very low linear correlations (r = 0.36), while in samles from 5 – 10 cm was found a moderate linear correlation. A reverse negative correlation between PCB content and moisture, was found in grass samples (r = -0.46) and hay samples (r = -0.60), while for the dry matter parameter the correlations were positive.

• For the correlation of fat content with the PCBs content from cow milk samples was found a strong negative correlation (r = -0.80), while for the sheep milk samples, the correlation between the two variables was strong positive (r = 0.73).

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RECOMMENDATIONS

� Monitoring the levels of dioxines and PCBs in raw feed materials during the drying process, but also in compound feed given tha fact that feed represints the major route by which food producing animales are exposed to contamination with these type of compounds.

� Monitoring the PCBs levels in environmentl matrixes (soil, vegetation) in sensitive areas to prevent the exposure of animales to these contaminants and selecting suitable areas for the traditional grazing.

OPENED PERSPECTIVES BY THIS STUDY The study conducted in this thesis opens new perspectives for future researches:

o Extending the researche to a different type of agri-food chain (production and process of meat and meat products).

o Evaluation of dioxins incidence (PCDD/F compounds) on the studied areas. o Monitoring also these compounds in the air matrix too.

ELEMENTS OF ORIGINALITY

• Monitorizaton of PCB compounds in the north-western of Romania (in the specific literature were reported results only for the east, south and west part of the country).

• Tracking the variations in dioxins and PCBs before and after some heat treatments in raw feed material and compound feed.

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SELECTED REFFERENCES

1. ACATINCĂI, S., 2004, Producţiile bovinelor (Ediţia a II-a), Ed. EUROBIT, Timişoara.

2. ARMITAGE, J. M., M. HANSON, J. AXELMAN, I. T. COUSINS, 2006, Levels and vertical distribution of PCBs in agricultural and natural soils from Sweden, In: Science of the Total Environment 371, 344–352.

3. BYUNG-HOON KIM, JIN-SUK JEONG, YOON-SEOK CHANG, 2003, Congener-specific distribution of polychlorinated dibenzo-p-dioxins, dibenzofurans and biphenyls in animal feed, In: Food Additives and Contaminants 20 (7), 659 – 667.

4. CADARIU ARINDA, 2005, Raportul de ţară privind Poluanţii organici persistenţi, http://eea.ngo.ro/materiale/CSR_final_rom.pdf

5. CHELEMAN ROWENA-ANA, MARIA TOFANĂ, DELIA TRUŢA, 2009, Preliminary actions in elaborating a Management Risk Plan to Reduce Dioxins Exposure through the Processing of UHT Milk. In: Bull. UASVM Agriculture, 66:2, 265-272.

6. CHELEMAN ROWENA-ANA, MARIA TOFANĂ, RALUCA POPESCU, LIANA-CLAUDIA SALAN ŢĂ, DELIA TRUŢĂ, 2011a, Polychlorinated biphenyls concentration in soil samples from pasture areas, In: Bulletin USAMV Agriculture, 68:2, 185-190.

7. COSTIN G. M. (editor), P. ALEXE, Gabriela BAHRIM, Liliana BANU, C. BICHESCU, Daniela BORDA, M. BULANCEA, A. CIOLAC, Nicoleta CROITOR, T. FLOREA, Luminiţa GEORGESCU, C. MAN, Carmen MORARU, C. MORARU, Ct. MORARU, Gabriela RÂPEANU, Rodica SEGAL, S. STANCIU, Nicoleta STĂNCIUC, 2008, Alimente ecologice – alimentele and sănătatea, Ed. Acadenica, Galaţi.

8. COVACI A., A. GHEORGHE, P. SCHEPENS, 2003, Levels of persistent organochlorine pollutants in soils from South Romania, In: Fresenius Environmental Bull. 12:2, 94–102.

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17. Council Regulation (EC) No 2375/2001 amending Commission Regulation (EC) No 466/2001 setting maximum levels for certain contaminants in foodstuffs, Official Journal of the European Union.

18. EPA Method 1613: Tetra-through Octa-Chlorinated Dioxins and Furans by Isotop Dilution HRGC/HRMS, 1994, Engineering and Analysis Division, Washington, D.C.

19. International POPs Elimination Network (INEP), 2008, An NGO Guide to Persistent Organic Pollutants, http://www.chemsec.org/.../ngo_guide_pops.pdf

20. ISO 5984/2002 – Furaje. Determinarea cenuandi brute. 21. National Research Council (NRC), 2003, Dioxins and Dioxin-like Compounds in the

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22. Resources Futures International (RFI), 2001, Persistent Organic Pollutants and the Stockhlom Convention: A Resources Guide,

http://siteresources.worldbank.org/.../PersistentOrganicPollutantsAResourceGuide2001.pdf 23. U.S. EPA, 2003, Exposure and Human Health Reassessment of 2,3,7,8-

Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds, Part I, Vol. 2, Washington, DC, http://www.epa.gov/ncea/pdfs/dioxin/nas-review/

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