H Barton
email: mfbarton@cyf-kr.edu.pl

Predicted intake of trace elements and minerals via household drinking water by 6-year-old children from Krakow (Poland). Part 3: Aluminum*

Keywords: Exposure assessment, GFAAS, Metals -uptake, Metals analysis -AAS Additives/Contaminants: Aluminium, Environmental contaminants, Food contact materials, Toxic elements drinking water, contamination, cement wells, water treatment, aluminum intake, children, health protection

In order to assess aluminium (Al) intake via drinking water by children, ca. 400 households in urban, peripheral and rural areas were investigated. in the period between 2000-2001. The samples of drinking water (DW) were collected by a double sampling method (morning, evening). Al was measured by graphite furnace atomic absorption spectroscopy (GF-AAS). Geometric means (GM in µg L -1 , C.I.)

were for evening samples: urban 72.5(13.2-398), peripheral 6.3(0.5-86.7), and rural 16.6(0.5-593). For most households the Al concentration in the morning samples was lowered by ca.

40% due to deposition of Al containing compounds during standing overnight. These deposits can be later taken up into the water. The EC Al limit of 200 µg L -1 for DW was exceeded for 7.7 %, 0 % and 13.2 % of households from urban, peripheral and rural area, respectively.

The predicted mean contribution of DW to Al intake by 6-year-old children was 0.38% of PTWI (Provisional Tolerable Weakly Intake, 7 mg kg b.wt. -1 ) for urban sites and less for the other ones. For those cases exceeding the Al limit, the predicted Al intake via water consumption contributed 2.30-4.21% of the PTWI. In urban areas the Al concentration was enhanced due to water treatment while in rural sites due to contamination from cement-made wells. The high prevalence of cement wells in rural areas suggest that drinking water from individual sources should be included in a safety control system. The quality of materials for construction of wells and containers for drinking water storage should be also improved. In order to limit Al intake from drinking water, this study also supports the suggestion to discard overnight standing water before it is taken for consumption, especially for the use for children.

Introduction

Aluminum (Al) is the third most abundant element on the earth and it is still considered to be safe for human health. This metal and its derivatives are widely applied in human life, including food and pharmaceuticals. On the other hand, toxic effects of Al have been known for a long time. Among them, dialysis dementia [START_REF] Alfrey | The dialysis encephalopathy syndrome. Possible aluminum intoxication[END_REF]), ferric-independent microcytic anaemia [START_REF] Touam | Aluminum-induced, reversible microcytic anemia in chronic renal failure: clinical and experimental studies[END_REF]) and osteomalacia [START_REF] Bushinsky | Effects of aluminum on bone surface ion composition[END_REF] are the major diseases caused by excessive Al accumulation. Normal Al blood concentration is low and was estimated at below 10 µg L -1 of plasma. Its 5-fold increase leads to manifestation of toxic effects. In recent years, pro-oxidant effects of Al in the brain and potential involvement of Al in Alzheimer disease and other dementias have been investigated. Although a causal effect of Al has not yet been identified, there is considerable experimental evidence in support of the role of aluminum in the formation of senile plaques [START_REF] Gillette-Guyonnet | Cognitive impairment and composition of drinking water in women: findings of the EPIDOS Study[END_REF].

Al intake from food ranges between 2-12 mg day -1 [START_REF] Noel | Determination of several elements in duplicate meals from catering establishments using closed vessel microwave digestion with inductively coupled plasma mass spectrometry detection:estimation of daily dietary intake[END_REF]) making an insignificant contribution to the PTWI (7 mg kg body weight -1 week -1 ) for adults). Normal daily Al intake was established at 5 mg day -1 [START_REF] Who | Guidelines for drinking-water quality[END_REF]). An estimated daily Al intake with the whole-day food rations in Poland, sampled in four cities was in the range between 0.6-15 mg day -1 [START_REF] Skibniewska | Diet monitoring for assessment of human exposure to environmental pollutants[END_REF]. Drinking water represents <10% [START_REF] Gillette-Guyonnet | Cognitive impairment and composition of drinking water in women: findings of the EPIDOS Study[END_REF] or 0.1 mg [START_REF] Yokel | Aluminum bioavailability from the approved food additive leavening agent acidic sodium aluminum phosphate, incorporated into a baked good, is lower than from water[END_REF] of total Al intake.

Al in water has significantly greater biological availability (0.3%, [START_REF] Yokel | Aluminium toxicokinetics: an updated minireview[END_REF] than that in solid food (ca. 0.1%, [START_REF] Yokel | Aluminum bioavailability from the approved food additive leavening agent acidic sodium aluminum phosphate, incorporated into a baked good, is lower than from water[END_REF]. In healthy adults, the availability of Al from the gastrointestinal tract is as low as 0.1% and its excretion is fast enough to keep body Al levels at the low natural level. Accumulation of Al is observed if this equilibrium is disturbed (e.g. kidney diseases and dialyses, excessive dietary intake or occupational exposure). Moreover, some dietary factors enhance Al absorption, for example, citric acid and other fruit acids or lactose, as opposed to the phosphates and silica. However, the gastric barrier in newborns and small children is less restrictive and Al intoxication was observed upon treatment with Al-containing antacids.

Natural water, untreated with Al salts, contains Al at concentration significantly below recommended upper limit in treated drinking water of 200 µg L -1 (WHO 1993) and rarely exhibits higher concentrations, except anthropogenic water pollution (Rowland et al. 1990, Exley and[START_REF] Exley | Severe cerebral congophilic angiopathy coincident with increased brain aluminium in a resident of Camelford, Cornwall. UK[END_REF]. This guideline value is founded not on health considerations but on aesthetic concerns of the water treated with Al salts. Due to the fact that Al compounds are used as phosphate binding and organic matter coagulating factors in water supply systems, the presence of soluble Al salts in drinking water is significantly fortified. [START_REF] Martyn | Geographical relation between Alzheimer's disease and aluminium in drinking water[END_REF] showed an increased risk of Alzheimer's disease (AD) in regions, where the Al concentration in drinking water was over 110 µg L -1 , but a little association between Alzheimer's disease and higher Al was found in another study [START_REF] Martyn | Aluminum concentrations in drinking water and risk of Alzheimer's disease[END_REF]. [START_REF] Rondeau | Relation between aluminum concentrations in drinking water and Alzheimer's disease:an 8-year follow-up study[END_REF] found no relation between Al and cognitive function, at the Al concentrations below 63 µg L -1 .

Similarly, [START_REF] Rondeau | A review of epidemiologic studies on aluminum and silica in relation to Alzheimer's disease and associated disorders[END_REF] from the review of several epidemiological studies concluded that not enough evidence supports a link between Al in drinking water and AD. Although -as the only 'hard fact' from many studies -the presence of the increased Al-containing deposits was found in brain in AD, causative role of Al has not been (e.g. [START_REF] Sparks | Trace copper levels in the drinking water, but not zinc or aluminum influence CNS Alzheimer-like pathology[END_REF]) and suppose may not [START_REF] Exley | Severe cerebral congophilic angiopathy coincident with increased brain aluminium in a resident of Camelford, Cornwall. UK[END_REF] be easily identified. The other factors are suggested to be involved in decreasing (e.g. [START_REF] Gillette-Guyonnet | The potential Influence of silica present in drinking water on Alzheimer's disease and associated disorders[END_REF]) and increasing AD risk (e.g. [START_REF] Solfrizzi | Macronutrients, aluminium from drinking water and foods, and other metals in cognitive decline and dementia[END_REF][START_REF] Sparks | Trace copper levels in the drinking water, but not zinc or aluminum influence CNS Alzheimer-like pathology[END_REF], 2007). Based on the current studies Al may play, at least, a role as cofactor of sequence of events, including oxidative stress, which can finally reach AD and other neurodegenerative diseases (see reviews: [START_REF] Solfrizzi | Macronutrients, aluminium from drinking water and foods, and other metals in cognitive decline and dementia[END_REF][START_REF] Miu | Aluminum and Alzheimer's disease: A new look[END_REF][START_REF] Rengel | Aluminium cycling in the soil-plant-animal-human continuum[END_REF][START_REF] Rondeau | A review of epidemiologic studies on aluminum and silica in relation to Alzheimer's disease and associated disorders[END_REF].

The question, if day-by-day Al intake from the same source, i.e. via drinking water, may be a risk factor of increased Al accumulation in human organism remains still unanswered.

Consequently, there is currently a debate within WHO about health criteria for Al level in drinking water [START_REF] Who | Guidelines for drinking-water quality:incorporating first addendum. Aluminium in drinking-water. Background document for preparation of WHO Guidelines for drinkingwater quality[END_REF].

This study was a part of the environmental research program in 6-year-old children in whom analysis of lead (Pb) and cadmium (Cd) was published earlier [START_REF] Barton | Predicted intake of trace elements and minerals via household drinking water by 6-year-old children from Kraków (Poland). Part 1. Lead (year 2000)[END_REF][START_REF] Barton | Predicted intake of trace elements and minerals via household drinking water by 6-year-old children from Kraków (Poland). Part 2. Cadmium (year 1997-2001)[END_REF]). This paper is 3 rd part of that series and presents the results of analysis of Al concentrations in household drinking water collected by a double sampling method [START_REF] Barton | Predicted intake of trace elements and minerals via household drinking water by 6-year-old children from Kraków (Poland). Part 1. Lead (year 2000)[END_REF] in the Krakow metropolitan area, surroundings and in rural areas in southern Poland.

The predicted aluminum intake by 6-year-old children is discussed.

Materials and methods

Drinking water (DW) samples analyzed in this study were collected from households at [Insert Figure 1 about here] We investigated ca. 400 households and preschools in the period between 2000-2001. The group was a part of population (70%) studied previously for cadmium [START_REF] Barton | Predicted intake of trace elements and minerals via household drinking water by 6-year-old children from Kraków (Poland). Part 2. Cadmium (year 1997-2001)[END_REF]. Water samples were collected at study sites located in apartment houses, private houses (248), preschools (150) and public buildings (11) in Kraków and in houses from rural areas (55). About 200 children (data completed for 181 children) for whom household's drinking water was analysed for aluminum were included in the current study.

The anthropometric data of the current study group of children and whole population studied were homogeneous. These data were published previously [START_REF] Barton | Predicted intake of trace elements and minerals via household drinking water by 6-year-old children from Kraków (Poland). Part 2. Cadmium (year 1997-2001)[END_REF]). Characteristics of children for current aluminum studies were for total study group as follows(mean±standard deviation): N=181, Gender structure: 42.7% Male; Age 6.6 ±0.6; Body weight 24.3 ±5.0 kg; Height 1.24 ±0.08 m; Body Mass Index 15.9 ±2.7 kg m -2 ; Adequate Water Intake (AWI ) 1.56 ±0.09 L day -1 (calculated from equation taken from [START_REF] Manz | The most essential nutrient:defining the adequate intake of water[END_REF].

The DW samples were collected twice a day by children's parents following our instruction, generally in the kitchen or a place from where water was taken for consumption, at the first

Chemicals and solvents

High purity water obtained from double water purification system Milli-Ro & Milli-Q (Millipore, 10 18 Ohm cm) was used for final washing of vials, and preparation or dilution of standards. Nitric acid 65% HNO3 (Merck, Suprapure) was used for preparation of standards or sample acidification. Magnesium nitrate Mg(NO 3 ) 2 (CertiPUR ® , Merck) and diammonium hydrogen phosphate (NH 4 ) 2 HPO 4 (AnalaR ® , BDH) were used as modifiers. Al standard solution containing 1000 mg Al L -1 from Merck was used for method calibration, while ICP Multiple Element Standard I (toxic elements, Merck, Nr.15474) containing 200 ± 20 mg Al L - 1 was used for routine quality control. All final standard solutions and samples for analysis were prepared in 0.5% nitric acid (v/v). All other details were described previously [START_REF] Barton | Predicted intake of trace elements and minerals via household drinking water by 6-year-old children from Kraków (Poland). Part 2. Cadmium (year 1997-2001)[END_REF].

Samples

The samples of water were collected in acid-washed 120 ml polyethylene containers and acidified with 65% nitric acid (0.5 ml/100 ml). All operations with samples and standards were performed in a clean room with air conditioning through four-cascade filter system with EU13 class end-filter and laminar hood class A, HS12 (Heraus, Germany).

Analysis of Al in water

Aluminum concentration was estimated by the flameless atomic absorption spectrometry (GF-AAS) using a Perkin-Elmer instrument with Zeeman background correction (5100ZL) by the adopted procedure from the method validated to the quality control of dialysis water.

Instrumental conditions: hollow-cathode lamp (HCL) working at 309.3 nm, slit 0.7 nm, transversely heated graphite tube (T-HGA) with Lvov platform. Each new graphite tube was preconditioned with 0.1% solution of zirconium nitrate. Temperature program (temperature in PC / ramp time in seconds / hold time in seconds / argon flow in mL min -1 ): 110/10/40/250, 130/10/5/250, 170/20/5/350(drying), 800/10/20/250, 1400/5/20/250(ashing), 2350/0/3/0(atomization), 2450/1/1/250(cleaning), 600/8/1/250 (user controlled end-cooling, slow down oxidative enlargement of sample inlet in tube). Peak area was measured as an analytical signal. Sample injection volume was 20 µL, modifier 1: 0.04M Mg(NO 3 ) 2 -6 µL, modifier 2: 0.6M (NH 4 ) 2 HPO 4 -4 µL (necessary for occasionaly present chlorides in DW).

Each sample was run in duplicate and the mean of two successive results at the relative

Calibration, post-measurement re-calibration and data correction

Method was calibrated using standard stock solution containing 200 µg Al L -1 and its successive dilutions in 0.5% v/v nitric acid to obtain four points. For blank, the two blank samples were measured to doubly statistical weight of background against the other standards.

Calibration curve was non-linear. The example of calibration curves is shown in Figure 2.

[Insert Figure 2 about here] As a primary results the concentration data were obtained from automatic routine mode.

Original integral signals were collected as well. The analytical data were subjected to postmeasurement processing consisting of re-calibration and correction for sensitivity drift with non-linear fitting procedures.

The following empirical equation was used for post-measurement re-calibration: we used Al concentration obtained from fitting procedure (B 0 ) rather than the measured value.

Peak Area = Injection Volume * Scaling constant * (C + B 0 ) [ 1+ a 1 (C+B 0 )] [(M 0 +a 2 (C+B 0 ))] -1 (1) where:
The fitting process was primarily controlled via characteristic mass, to be within the normal range of 10-50 pico-g Al (manufacturer's M 0 value 31 pg Al). The resulting equation was further tested for continuous shape below and over calibration range and equation fittings with Cyclic stability control of analysis was applied using the water sample with Al concentration of 50 µg Al L -1 , which was run repeatedly after every fifth samples (ten runs). These measurements were used for sensitivity correction by the use polynomial or mixed equation.

Characterization of the method

The method of analysis has been controlled within the framework of "Worldwide Interlaboratory Aluminum Quality Control" programme (WIAQC/France, [START_REF] Guillard | French external quality assessment schemes for lead in blood and aluminum in plasma and dialysis water[END_REF] from 1997 until 2006. Our laboratory was ranked within the acceptable groups.

The limit of detection was 0.6 µg L -1 (3SD for blank). Instrumental precision (SD) estimated from a large number of measurements at Al <10 µg L -1 was 0.7 µg L -1 . The analytical precision (SD) for laboratory DW Al quality control standard containing 50 µg Al L -1 was 1 µg L -1 . The within and between day precision was 2.4% and 5.8 %, respectively. Recovery: mean (C.I.), 97% (91-103), N=6.

Aluminum intake calculation

The weekly aluminum intake was calculated for each individual child according to the following equation (parameter ± uncertainty):

Weekly Al intake = 7 days * (1.5 ± 0.25 L of water daily requirement) * (Al concentration

[µg L -1 ] ± 21 %) [Body weight ± 0.5 kg] -1 .
The parameter uncertainties were derived from key steps of the procedure of analysis of Al concentration or as combined imprecision of measurements and population variation (see: [START_REF] Manz | The most essential nutrient:defining the adequate intake of water[END_REF]. For Al concentration uncertainty within-year precision 21% was set as experimental long-term value. Daily water requirement for 6-year-old children was calculated from the equations according to the literature (e.g. Gaspar 1999) as adequate water intake (AWI) and verified by comparison with reference data (Dietary Reference Intakes 2004; [START_REF] Manz | The most essential nutrient:defining the adequate intake of water[END_REF]. The water intake approximated the volume of 1.5 L day -1 was applied for Al intake calculation. The cumulative uncertainty of the Al intake with DW was approximated as a sum of particular relative contributions calculated with the total differential of the above equation, then doubled to the coverage of 95% (Ellison et al.(ed.) 2001). The cumulative uncertainty of Al intake was estimated at 79%.

Relative intake was calculated as percentage of water Al intake vs. Provisional Total Weekly Intake (PTWI) value of 7 mg Al week -1 kg body weight -1 .

The Al concentrations were evaluated against the current obligatory in Poland limit for Al in DW of 200 µg L -1 (Polish Ministry of Health and Social Welfare 2000, WHO 1993, Council Directives 98/83/EC, 80/778/EEC) for water supplied by Water Treatment Plants (WTP) and similarly for individuals sources for which the limit was not obligatory.

Analytical data were prepared for statistical evaluation by replacing the values lower than the limit of detection by the value equal to the half of the limit of detection (LOD/2, i.e. 0.3 µg Al L -1 ). The evening water samples (W2) were considered as material representing the major (through the day) pool of consumed water by people, for which the results were primarily used for calculations of intakes and discussed, if not stated otherwise. The above parameters were linked by the following equations useful for recalculations:

Statistics

D 1,2 = Al W1 * (1-1 / R 1,2 ) = Al W2 * (R 1,2 -1) D 1,2 %= 100 * D 1,2 / Al Wmax; where: Wmax = W1 for D 1,2 >0; Wmax = W2 for D 1,2 <0 D 1,2 %= 100 * (1 -1 / R 1,2 ), for Wmax = W1 D 1,2 %= 100 * (R 1,2 -1), for Wmax=W2

Abbreviations

• Areas -site groups: U -urban (metropolitan) sites of Krakow; P -peripheral areas of the city (surroundings, neighboring or similar towns); R -rural area, combined sites;

NU -non-urban sites -combined suburban, peripheral and rural sites;

• Groups: children -depending on context this term comprises children and water collected in their households; households -comprised households with children investigated and without children (other households without children for which water was collected, preschools of children, public buildings included in the study); cements wells -CW: identified households in which water was taken from cement-made concrete.

• Fraction 'a' and 'b': this term was used to describe two kinds of households for which

Results

Aluminum concentrations in drinking water

Within the study period (2000)(2001), Al concentration did not differ significantly. The Al concentrations were found below 1233 µg L -1 for the morning and 1893 µg L -1 for evening samples. For two major types of sites, i.e. U and NU, GM values for W2 (W1) were 72.5 (48.8) µg L -1 and 9.3(4.9) µg L -1 , respectively. The obtained data was further analyzed as whole population or in different subgroups including U, P and R groups. The GM values of Al concentrations were collected in Table I.

[Insert Table I about Comparison of both histograms suggests that at each type of site, the frequency maximum for evening water is shifted to higher Al concentrations versus morning water samples. This effect was manifested also by high number of outliers in NU sites below LOD value for morning water (W1: 17.9%, W2: 0.8%). This indicates that morning water is poorer in Al than evening one. The mechanism of this effect is discussed below. Frequency of log-transformed Al concentrations in urban group exhibits normal distribution profile for the majority of samples within the range 20-500 µg L -1 . Non-urban group shows normal profile for the whole data range, without outliers (<LOD), as deduced via Kolmogorov-Smirnov test. The U and NU profiles exhibit major maxima in different Al concentration ranges but both profiles partially overlapped. However, the profile in Figure 3b suggests that distribution of Al concentration in water may be generally multi-modal.

Sources of Al in supplied water (W2)

The drinking water in Krakow urban area is supplied from five major sources as shown in values of Al concentration in water from households depended on the supply system and were in the range 8.8-63.0 µg L -1 for W1 and 18.9-82.9 µg L -1 for W2 (evening) samples. Mean Al concentrations in water supplied from different sources were shown in Figure 4.

[Insert Figure 4 about here]

The percentage of contribution of major suppliers to the water consumption is shown in Figure 4 and other data discussed below were taken from report of [START_REF] Zimoch | The water production subsystem failures affects of Krakow's water supply system[END_REF]. Three water treatment plants produce DW from different sources and water is treated with aluminum sulphate (A-C). The median Al concentrations in W2 samples from these systems were practically the same (AlW2: 65-83 µg L -1 , GM 69.9). There were also small local areas in Krakow where mixed water from these three sources is supported (see Figure 1, overlapped areas) for which Al concentration is similar (AlW2 64.0 µg L -1 ). Water from Sanka River (D)

is not treated with Al salts and daily apparent exchange rate in storing tank (made of steelconcrete) is high (84% a day). These explain why this source supplies high quality DW that contains as low Al concentration (11 µg L -1 ) as that from non-urban sites ( The double sampling system (morning and evening) allows distributions of Al concentrations in morning samples (W1) to be plotted against concentrations in evening samples (W2) (Figure 5). Every point on the plot represents one household investigated, for which abscissa shows values for evening water, and ordinate values for morning water. The U-and NUhouseholds were separately shown using different symbols. The two cut-off lines in Figure 5 refer to the Al concentration of 200 µg L -1 , as the current maximal Al concentration limit in drinking water according to the European Community directives and Polish regulations.

[Insert Figure 5 about here]

The cut-off lines divide the plot into four sectors A, B1, B2, C. The points in the sector A refer to the households for which Al concentration fell below the limit (200 µg L -1 ) for both, evening and morning samples. Most of households from urban (91.8 %) and non-urban group (93.7 %) were located within this sector. The contribution of the other sectors to the U-and NU-sites was shown in Figure 5.

Households for which Al concentration in morning (W1) or evening (W2) water samples were higher than the limit belong to the sector B1 (1.8% for U, 0% for NU) or sector B2 (5.5% for U, 0% for NU), respectively. In the sector C, there are only few points for which Al concentration exceeded the limit, both in morning and evening samples, mainly collected in the non-urban area (0.9% for U, 6.3% for NU). In the major fraction (contribution of 80% of the samples), a drop in Al concentration was observed (plot 'b'). The second "mirror-like" plot 'a' was obtained for the minor fraction (contribution of 20%) in which an increase in Al concentration in W1 was observed. In order to show statistical weight of the data, number of households, for which a particular mean was calculated, was indicated in the plot 'a'.

Influence of internal pipeline system on Al concentration in tap water

[Insert Figure 6 about here]

In the major fraction , plot 'b', there is a near-linear decrease in Al concentration during stagnancy (in W1) with the increase in Al level in water from the supply system (W2). The plot 'a' , representing fraction of households where Al increased in W1 -is also almost linear, however, the slope is twice higher than that for 'b'.

Explanation of this major effect is based on easiness of hydrolysis of soluble Al salts, which can produce subtle dispersed particles. These particles may undergo sedimentation or adhesion to the pipeline surface during stagnancy. This process can be reversed by ejection of these particles to the water during the morning usage. The contribution of the minor subset 'a' (20%) was deemed significant and worth of at least further discussion. Possibly, a certain number of points in 'a' fraction may be due to a random analytical error for pairs of samples having close Al concentrations in W1 and W2. These points may be situated near over or below the line 1:1 in Figure 5, i.e. the line of theoretical equality of W1 and W2. These points may correspond to both low and high W2 Al concentration, but always the value of parameter D 1,2 is small. However, there were significant positive D 1,2 values, which could not be explained by analytical uncertainty. Also a trivial mistake in sample description can probably be excluded. Roughly, this increase in Al concentration in W1 occurred for one of five W1 samples (20%). However, if the data for 'a' fraction were further filtered via rigorous quality criteria, the number of cases was reduced to 11%, and this number is still significant. The presence of the 'a' fraction was tentatively interpreted as an effect of removal of the A hypothetical possibility of relatively high Al concentrations in W1 (as mentioned above) is also visible from the left side of plot 'a' in Figure 7, where contamination level D 1,2 % exceeded the Al concentration in W2 by 90%. On the other hand, for expected random mechanism of ejection of the deposits into the water, the repeatability of this process and probability to obtain comparable samples in a particular household may be poor and this phenomenon can only be observed on population scale.

[Insert Figure 7 about here]

The major fraction comprising 80 % of households is presented in plot 'b'. Similar analysis of contamination and deposition for different subgroups is shown in Figure 8.

It is evident that there was a strong concentration dependency of both deposition and contamination in urban samples. Shapes of plots for U and NU sites were significantly different. The magnitude of deposition and contamination for U site was higher then those for the NU sites and cement wells. On the other hand, plots for both NU and cement wells exhibited similar shapes, besides, the Al concentration for the later group was higher by an order of magnitude (see Table I).

[Insert Figure 8 about here]

Aluminum intake with drinking water by children

Mean Al intakes with drinking water by 6-year-old children in selected subgroups were shown in Table II.

[Insert Table II about here]

The Al intakes were calculated using anthropometric data of study group of children.

Relative intakes were expressed as a percent of current Provisional Tolerable Weekly Intake (PTWI, 7 mg kg body mass -1 per week, for Al intake from all sources). In the urban group (U), predicted supply of Al from morning (section A) and evening (B) drinking water was (GM) W1: 0.28 and W2: 0.38% PTWI. In peripheral and rural group (non-urban), the predicted intakes were negligible (below 0.1%) for both, morning and evening water. The difference between Al intake by children from urban and non-urban sites was highly significant.

However, for selected subgroup from non-urban sites, where water was drawn from cementmade individual wells (CW), the intake predictions (A4, B4) were higher, W1: 0.16 and W2: 0.22 % PTWI and comparable to that for U-sites.

The subsets U and CW did not differ (W1: p=0.25, W2: p=0.15, Kruskal-Wallis test). Both these groups, however, cannot be considered similar (in terms of Al concentration) due to a very wide range of Al concentrations for wells in comparison to urban samples (see Table I).

Consequently, large percent of samples from cement wells exceeded the Al limit of 200 µg L - 1 in NU sites(5.4% of households), but 13.2% at R site( was calculated for CM group the limit was exceeded in 24.1% households.

Table III presents data of Al intakes for 'risk groups' of children, who drink water containing Al concentration exceeding the limit.

[Insert Table III about here] The estimations were performed both, for group of households with children participation (section C1, designated as 'children') the all households studied (C2). The whole group was composed of households, with and without children(e.g. preschools). Potential Al intakes for the households without children, were calculated using necessary anthropometric data derived from mean values for the same or similar areas and for gender structure observed for a particular site. It can be proved by comparison of data from section C1 (children) and C2 (all samples), that the resulting parameter values, i.e. real and simulated, were similar. Such a comparison made for the other groups (not included in Table III) gave the same conclusion, thus this approach could be applied for the further data analysis in groups, where there was not enough dealing with children(Table II, section C3, D4, D3, D4).

Al intake prediction for households, where the Al limit in all W2 was exceeded ('risk' group), reached 2.52% PTWI (section C1) for the group with children (or similarly 2.30% for simulation for all households, section C2). For NU group (C4), the Al intake reached the value of 4.21% PTWI, i.e. the value twice higher than for U site (C3).

For the tentatively suggested new limit of Al concentration in water of 100 µg L -1 (section D), the mean Al intakes appeared about twice lower, however, confidence intervals were comparable. On the other hand, the percent of samples exceeding the new limit was only 2-3 times higher. This indicates that compliance with to the new limit would be realistic and achievable. regulations do not apply to small or individual water supply systems and may not account for temporary fluctuations or undulations of water quality since they may remain unidentified.

Discussion

The above limit was established based on aesthetic concerns, not on consideration of Al health effects, thus it can be considered to be a temporary regulation. In some US states, local regulations permit maximum Al limit to be as high as 1 mg L -1 (California Regulations Related to Drinking Water 2006). It is worth noting that new WHO drinking water quality guidelines are under discussions and maximum Al limit based on health consideration is being debated [START_REF] Who | Guidelines for drinking-water quality:incorporating first addendum. Aluminium in drinking-water. Background document for preparation of WHO Guidelines for drinkingwater quality[END_REF].

About half of population in Poland and in other countries receive their water supply from uncontrolled small individual sources (e.g. [START_REF] Barron | New approaches to safe drinking water[END_REF]. The problem of quality of drinking water for these populations is discussed in many studies (e.g. [START_REF] Harrison | Chemical contamination of private drinking water supplies in the West Midlands, United Kingdom[END_REF].

In our study the difference between urban and non-urban areas was found significant. In NUareas DW is supplied from a variety of small local or private wells or springs without obligatory quality control. Therefore, household rural Al levels was anticipated to be lower than urban levels due to the lack of Al sources and treatment with Al salts. Our studies showed repeatedly [START_REF] Barton | Predicted intake of trace elements and minerals via household drinking water by 6-year-old children from Kraków (Poland). Part 2. Cadmium (year 1997-2001)[END_REF], however, that other sources of water contamination existed in these areas. It also appeared that rural area was not uniform also in terms of Al levels, and consisted of two sub-fractions related to water source, and, particularly, to the material from which storage container was built. The increased Al concentration was observed in water from cement made individual wells. This was probably due to poor quality of cementcontaining concrete, low quality (porosity) of well surface, non-neutral pH of water and low exchange rate of water in the well (large volume of stored water in comparison to daily use), and high ratio of well surface to water volume. In few cases, it appeared that larger storage containers, supplying water to more than one household, made also from concrete, did not show so high increase in Al level, probably due to higher exchange rate or/and lower volume/surface ratio.

Another observation in our study was significant differences in Al concentration between the morning and evening samples. This was a result of significant lowering of Al level in water stagnating in pipelines due to particle sedimentation. The yield of this process increased at lower and higher Al concentrations, both for treated (Urban sites) and untreated water (NU- sites ). However, there was a difference between urban (D 1,2 % ca. 60%) and non-urban sites (D 1,2 % ca. 40%) in the maximum. This indicates that chemical composition of Al-containing compounds in U (Al salts) and NU (leached from cement Al compounds) water samples is different. The fact that in some NU households water was taken directly from private individual wells located in neighbourhood or via a short pipeline system, may, in part, explain lower mean difference between W1 and W2 water samples.

Deposits on pipeline surfaces are known to enhance metallic pipeline corrosion processes and thus the release of heavy metal ions into the water. Furthermore, Al containing precipitates may contain also toxic elements as a result of typical co-precipitation or occlusion on the Alcontaining particles [START_REF] Violante | Coprecipitation of arsenate with metal oxides: nature, mineralogy, and reactivity of aluminum precipitates[END_REF]. Release of such a concentrate into drinking water may occasionally produce complex toxic mixture. Besides, although the Al concentration is generally lowered in the morning water, therefore, all together suggested to discard some amount of overnight standing water, if it is taken for consumption, to omit peak Al concentration in the morning usage as well as to decrease intake of the other toxic metals (see: [START_REF] Barton | Predicted intake of trace elements and minerals via household drinking water by 6-year-old children from Kraków (Poland). Part 1. Lead (year 2000)[END_REF][START_REF] Barton | Predicted intake of trace elements and minerals via household drinking water by 6-year-old children from Kraków (Poland). Part 2. Cadmium (year 1997-2001)[END_REF]).

It should be mentioned that Al leaching from cement mortar is known process and caused serious intoxications. The mortar used for building dialysis water pipeline was the reason of Al intoxication and deaths of several dialyzed patients [START_REF] Berend | Acute aluminum encephalopathy in a dialysis center caused by a cement mortar water distribution pipe[END_REF]. This leaching was enhanced due the aggressiveness of the distilled water, but it may also be dependant on other Al intakes calculated for a study population of 6-year-old children were very low, far below 1% of PTWI (see Table III). In U-site the mean intake accounts by 0.38% PTWI (91.8 µg Al day -1 ) and this level was supported by literature, i.e. 0.1 mg day -1 , e.g. I, III). It should be emphasized that PTWI was not exceeded in any of the particular cases. The Al intakes estimated for tentatively selected 'risk group' suggested that it would be reasonable to propose a new limit of Al concentration in DW, namely 100 µg L -1 (Table II, section D), which would give about twice lower intake value than intakes estimated for the current Al limit. This was due to a higher number of children included in 'risk group' and these additional households had lower water Al concentrations (100-200 µg L -1 ). Consequently, the percent of samples exceeding the new limit was two-three times higher. On the other hand, quartile and confidence intervals were not much affected by these revised estimations.

The above simulation was done, because there is growing evidence that Al, including soluble Al added to drinking water, elicits potentially harmful effects. Apart of potential effect of residual amounts of Al in drinking water, itself, water treatment with Al salts probably significantly removes silica from water due to formation of insoluble aluminum silicates. The silica is suggested to be one of the cofactors, which may reduce the risk of developing AD in elderly [START_REF] Jacqmin-Gadda | Silica and aluminum in drinking water and cognitive impairment in the elderly[END_REF][START_REF] Belles | Silicon reduces aluminum accumulation in rats: relevance to the aluminum hypothesis of Alzheimer disease[END_REF][START_REF] Gillette-Guyonnet | Cognitive impairment and composition of drinking water in women: findings of the EPIDOS Study[END_REF][START_REF] Gillette-Guyonnet | The potential Influence of silica present in drinking water on Alzheimer's disease and associated disorders[END_REF]), however it is controversial. Silica present in drinking water may bind aluminum and modify (possible decrease) its normal availability and circulation in the body.

At present, new technology allows for water treatment without the use of high concentrations of Al salts (e.g. the use of poly-aluminum-chloride (PAC) or -sulphate (PACS)). Moreover, at present water treatment methods, the limit of 100 µg L -1 can probably be easily be achieved, since the exceedance magnitude found accounts for 31% in urban area studied (Table III, section D3). This confirmed independent opinion presented in Guidelines for drinking-water quality: incorporating first addendum [START_REF] Who | Guidelines for drinking-water quality:incorporating first addendum. Aluminium in drinking-water. Background document for preparation of WHO Guidelines for drinkingwater quality[END_REF].

It should be emphasized, that DW is an everyday factor affecting populations. Several substances, e.g. citric acid [START_REF] Fulton | Absorption and retention of aluminum from drinking water. 1. Effect of citric and ascorbic acids on aluminum tissue levels in rabbits[END_REF] Hopefully, according to current debate among [START_REF] Who | Guidelines for drinking-water quality:incorporating first addendum. Aluminium in drinking-water. Background document for preparation of WHO Guidelines for drinkingwater quality[END_REF], there is intention to introduce health-based limitation for Al in water. Consequently, aluminum has been placed in the priority list of food additives and contaminants proposed for evaluation by JECFA(Joint FAO/WHO Expert Committee on Food Additives) for toxicity and intake of from its use in food additives and from other sources (FAO/WHO 2004).

.

Conclusions

1) The WHO drinking water quality guideline value for Al is 200 µg L -1 . Comprehensive analysis of health and technological aspects of the presence of Al in drinking water argues for lowering the maximum Al limit permissible in drinking water, for example by half, i.e.

to 100 µg L -1 . It is unquestionable that Al is involved in Alzheimer's disease and other forms of dementia. The long-term effect of low Al concentrations on health is not clear.

There are no technological barriers that would preclude a decrease in the Al level by applying water treatment with low Al methods.

2) Two major sources of Al in drinking water were identified and their extent was improve the quality of materials and concrete for construction of wells and containers for drinking water storage.

3) It was found in this study that Al forms deposits on the pipeline surface in stagnant period (for example during the night). Further, some deposits may be flushed with the first portion of water taken after standing. In order to limit Al intake from this contamination source, similarly as it was deduced in former Pb and Cd studies, it may be suggested to discard some of this standing water before it is taken for consumption, especially for the use for children.

4) The double sampling method and data analysis applied in this study proved to be useful for describing the changes in metal concentration in water, both in the case of its increase (contamination) or decrease (deposit formation) during a stagnancy period. 1. Figure 1 The study areas and water supply systems in Krakow.

F

Figure 2 Example of calibration curves and numerically fitted equation for analysis of

Al in water by GF-AAS method.

3. Al intake was calculated at assumed average water consumption 1.5 L day -1 by 6-years-old children.

PTWI (Al) = 7 mg (kg body weight) -1 week -1 , GM -geometric mean; SDg -geometric standard deviation; 95 % confidence interval was calculated from geometric standard deviation ‡ number of cases in selected category and its percentage in given group (exceedance in %)

&: group of households with children and without children (see text for explanation). † Note: values in section C4 (and D4) are the same as for non-urban(NU) and cement wells subgroup, because all samples in NU-sites which exceeded the limit are from cement wells.

  Kraków and rural areas of southeastern Poland: Urban sites (U)metropolitan area of Krakow with high apartment houses and large water supply system, which comprised major administrative districts of the city and could be divided into two subgroups: a) Krakow centre, Debniki, Krowodrza, Nowa Huta supplied by hard water from three local rivers, b) Borek Falecki and Prokocim supported by the soft mountain water from the Raba River. Non-urban sites (NU) -small spread-out public buildings or private houses supplied by local public or private water supplies and wells. This category included: Peripheral sites (P) -housing estates located on the east side of Kraków (Nowa Huta), Alwernia (a town to the west near Kraków), and small set of houses in Miasteczko Slaskie, all influenced by heavy industry. Rural areas (R) -non-industrialized towns and neighbouring village areas in southeastern Poland ca. 200 km away from Kraków. The study groups in urban Kraków area comprised all five major water treatment plants, supplied by different sources: Raba (A) River in Dobczyce, local rivers: Rudawa (B), Dlubnia (C) and Sanka (D) and deep well in Mistrzejowice (E).The study areas and water supply systems in Krakow are shown in Figure 1 (adapted from: Zimoch 2005).

  the morning (W1) and in the same or preceding evening (W2) after daily use.

  RSD) not exceeding 5% were accepted as estimation.

C

  B 0 , M 0 , a 1 , a 2 were estimated via fitting the equation 1 to the calibration measurements: B 0 -calibration background Al concentration M 0 -characteristic Al mass (analyte mass per 0.0044 integral absorbance) a 1 and a 2 -slope and shape constants of calibration curve, respectively Numerical fitting of four above parameters was performed by the use of non-linear fitting module of Statistica (Statsoft, Inc.) package. For background correction of calibration curve,

  were not accepted. The Al concentrations were calculated from final equation 1 by iteration and the resulting values were processed in the next step described below. The example of calibration equation and curves drawn from fitted equations were shown in Figure2.

  Statistical analysis was performed using Statgraphics or Statistica for Windows(StatSoft, inc.) packages. Both nonparametric (for concentrations) and parametric tests (for logarithmic values) were applied, if applicable. All mean values were geometric means (GM) or medians (MED, if not stated otherwise. For populations of not sufficiently uniform type of distribution or not defined, both, GM(or arithmetic mean, mean) and median values were discussed. The range of values, which included of 95% of population (95% confidence interval, C.I.) were estimated from lognormal distribution profile as GM x 10 (±1.96 * SDg) (or from frequencies as 2.5 and 97.5 percentiles). The t-test for normal distribution profile or Kruskal-Wallis ANOVA was commonly used and for multiple tests the more rigorous criteria of significance (higher value of a p-level) was considered.DefinitionsRelated parameters derived from Al concentrations in W1 and W2: R 1,2 : ratio of Al concentration in the morning to evening samples -coefficient of relative difference of the both Al concentrations -percentage of metal amount attributed to contamination of water (D 1,2 >0) or deposition of metal from water into plumbing (D 1,2 <0): D 1,2 %= 100 * (Al W1-Al W2) / Al Wmax; where Al Wmax = Al W2 or Al W1, whatever is higher

•

  Al concentration was changed during over-night standing -fraction 'a' -Al concentration was higher in water samples in the morning than in the evening (W1 > W2) as the effect of contamination -fraction 'b' -Al concentration was lower (or equal) (W1<=W2) as an effect of deposition Al from water to pipeline DW -drinking water • W1 and W2 -drinking water samples collected in the morning (W1, equivalent to stagnant water) and evening (W2, equivalent to flushed water) water samples, respectively; • AlW1 (or AlW2) -Al concentration in W1 (or W2) samples • WTP -water treatment plant • LOD -limit of detection • Statistical parameters: AM -mean (arithmetic mean), GM -geometric mean; MEDmedian, SD -standard deviation; p-level -statistical significance of particular test; SDg -geometric standard deviation coefficient; C.I. -95% confidence interval of the mean.

  here] Frequency distribution of Al concentrations in water samples collected early in the morning as stagnant tap water (first flush draw, W1) and in the evening as fully flushed water (W2) is shown in Figure 3a for urban group and Figure 3b for non-urban group [Insert Figure 3 about here]

Figure 1 .

 1 Figure 1. The largest area (A) is supplied by water from mountain Raba river, other areas by three local rivers (B-C) and the last one by water from a deep well (E, Mistrzejowice). Mean

Figure 5

 5 Figure 5 also shows two correlation lines. Line designated as 1:1 was drawn assuming theoretical situation for which both Al concentrations, in the morning and evening samples are equal. Regression line represents real linear regression between logarithmic values of morning and evening Al concentrations (r = 0.86). This regression line is located below the theoretical one, what means that morning water samples contained, in general, lower amounts of Al. This discrepancy increases with elevation of Al concentration in W2.

Figure 6

 6 Figure 6 shows the effect of water standing overnight in pipelines on Al concentration for different Al concentrations. This effect of is plotted as a change of Al concentration in W1 (D 1,2 ) versus Al concentrations in W2 because it can be assumed that this water is not influenced by internal pipelines and, possibly, it is comparable to that is produced by WSP (see ref.: Reports on water quality in Krakow). The plot was constructed by grouping the

  WHO drinking water guideline value for Al is 200 µg L -1 (WHO 1993) and in Poland, the value of 200 µg L -1 has been obligatory from October 2000 (Polish Ministry of Health and Social Welfare 2000). This regulation implements Council Directive 98/83/EC that has been in force in Poland since the accession to the European Community (EC). According to this directive, drinking water should be controlled only in larger water supply systems. These

  factors for natural waters(e.g. pH) as well quality of container surface.Based on Al concentrations and water sources, all households can be grouped into three categories, homogenous for Al concentration in DW, i.e. two groups at enhanced Al concentrationurban sites (U), cement wells (CW) and one at low, almost natural Al concentration -nonurban other sites (NU other).

  described in this study (a) water treatment with Al salts and (b) contamination from cement-made wells. The enhanced Al concentration in water from cement wells and high prevalence of wells in rural areas suggest that drinking water from individual sources should be included in a quality control system. Moreover, it would be recommended to

Figure 3

 3 Histograms of aluminum concentrations in household drinking water for samples collected in the morning (W1) and evening (W2): upper plot -urban areas, lower plot -non-urban areas 4. Figure 4 Mean Al concentrations in drinking water samples from study areas according to the major supply systems in Krakow. 5. Figure 5 Two-dimensional distribution plot of Al concentrations in morning (W1) vs.evening water samples (W2), for urban (U, circles) and non-urban households (NU, spots), and log-linear regression for the whole data set.6. Figure6Influence of Al concentration (the whole population) on parameter D 1,2 for fraction 'a' (contamination) -upper curve, and fraction 'b' (deposition) -lower curve 8. Figure7Influence of Al concentration (the whole population) on parameters R 1,2 and D 1,2 %. Figure8Influence of Al concentration on parameter R 1,2 and D 1,2 % for selected subsets: U-urban, NU-non-urban and cement wells (CW).

  : D 1,2 % parameter is calculated here by division of D 1,2 value by Al concentration in W2 -a higher value from both (W1, W2) in the case of decreasing influence of stagnancy on aluminum. Due to a direction of transport process from water to pipeline (deposition), they are named deposition parameters, here, consistently with contamination parameters for reverse direction of metal transport(Pb, Cd)

  However, the percentages in column 'N' given in parentheses are different # These values represent current state at regulation in force at Al limit of 200 µg/L

Table I

 I

	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	, NU others, non-cement wells). The water from system E (deep well) is not treated with Al salts but O n l contains ca. 30 µg Al L -1 . It appears that this system supplies ca. 2% of whole drinking water y
	in Kraków and the water is stored in cement-concrete container. The daily exchange rate of
	ca. 32% was small enough for extraction of Al from container wall to the water, similarly to
	that in cement wells. Figure 4 shows also contributions of a particular system to total DW
	demand in metropolitan Kraków. These data indicate that 84% of water consumed in Kraków
	contains Al ca. 70 µg L -1 .
	Comparison of aluminum concentration in morning and evening samples of drinking water

  concentrations as compared to high Al levels, higher size of particles present in low Al concentration range or co-precipitation with other water constituents or metals.

	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	The plot shows maximum between ca.15-80 µg Al L -1 in W2, what means that the lowest Al deposition occurs in this Al concentration range. Namely 40 % of Al present in W2 water is deposited in O n l the pipeline (maximum R 1,2 at ca. 0.6 and D 1,2 % ca. -40). That range of Al concentration matched those samples from urban population investigated down to the median value (Table y
	I, W2).
	Plot 'b' (D 1,2 %, Figure 7) proved that Al deposition was enhanced in water both at highand

low Al concentrations outside of plot maximum, where 50-70% of Al was precipitated. It is surprising that this precipitation occurred also at low Al concentration. This may be tentatively explained by increased degree of hydrolysis of soluble Al salts at lower

Table I

 I

	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	O n l
	y
	, section C4). If this parameter

  Gillette-Guyonnet 2003) have shown that Al in DW at 100 µg L -1 may be correlated with an increased risk of Alzheimer's disease. There are many studies on neurotoxicity of other metals from which copper, aluminum or both in combination induced oxidative stress in the brain[START_REF] Becaria | Aluminum and copper in drinking water enhance inflammatory or oxidative events specifically in the brain[END_REF]. Current knowledge on influence of low Al on health is unclear and possibly the progress in solving that problem and defining long-term toxicity may still be slow. Taking into consideration that natural Al levels in drinking water are low and that natural Al-containing compounds are insoluble Al-silicates, while water treatment enriches it with soluble Al compounds, this is a 'risky' and doubtful rationale for keeping high Al concentration limit in drinking water. It is suggested to consider aluminum to be potentially harmful until its role and interactions in the organism are clarified and natural low body Al level is rationalized. In other words, adequate 'margins of safety' should be adopted not only for health criteria based on Al toxicity but also for current judgements of Al low-level Al accumulative toxicity.

	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	O n l
	, and juices habitually added to the tea y
	and drinks, are able to increase availability of Al via gastrointestinal tract. Moreover, gastric
	barrier for Al availability in newborns and small children is much weaker, what was a cause
	Al intoxication after the treatment newborns with antacids. The drinking water quality is
	especially important health affecting factor for children of the formula based diet prepared
	from dry product using drinking water.
	There is still a debate about health implications of currently so-called 'safe' Al
	concentrations in drinking water. Epidemiological studies of (e.g. Rondeau et al. 2000,

Table I

 I Al concentration in drinking water and related parameters 1 Values of related parameters are given for fraction 'b' of data (D 1,2 <=0 i.e. W1<=W2, major fraction: 80% of samples) 3 : Proper sign was added to the final values after statistical operations performed on absolute values

	Group				Concentration							Related parameters 2		Percent
											(fraction 'b': D 1,2 <=0, deposition)
	or Parameter			[µg L -1 ]				p-level 1	Concentration	Ratio	Difference	of samples
										W2 2		(R 1,2 )	(D 1,2 ) 3	exceeding the
																limit
		Morning water samples (stagnant)	Evening water samples (flushed)	W1	(µg L -1 )		W1/W2	W1-W2	(W1-W2)	(200 µg L -1 )
			W1				W2		&W2				[µg L -1 ]	W2 4
														[%]
		GM	N SD	Quartiles	GM	N	SDg	Quartiles	Concent	GM	N	GM	GM	AM	W1	W2
		(Median	g	(95% c.i)	(Median)			(95% c.i)	rations	(Median)		(Median)	(Median)	(Median)
		)							(varianc
									e)
	Urban ( U )	48.8	240 2.3	34.5-72.7	72.5 (82.2) 246 2.38 53.3-106.0	***	79.3	205 0.52 (0.58) -28.2 (-31.3) -42.8 (-42.1) 2.5	7.7
		(54.8)	9	(8.9-268.5)				(13.2-398)	(0.98)	(85.4)
	Peripheral	2.5 (2.1) 64 5.4	0.6-8.5	6.3 (5.0)	77 3.82	3.1-11.2	***	6.2 (5.0)	66	0.26 (0.25)	-2.9 (-2.8)	-61.5 (-74.8)	0	0
	( P )		5	(0.1-70.6)				(0.5-86.7)	(0.06)
	Rural ( R )	13.0	42 9.6	2.5-49.4	16.6 (8.9)	53 6.20	4.3-36.3	0.56	17.0 (9.2) 42	0.48 (0.60)	-5.7 (-4.2)	-41.3 (-39.7) 16.7 13.2
		(11.2)	1	(0.2-1096)				(0.5-593)	(0.14)
	p-level: 1	***			**					**		**	*	*
	P vs.R	(*)			(*)					(*)		(0.16)	(*)	(*)
	Non-urban	4.9 (4.1) 106 8.0	1.1-17.7	9.3 (6.5)	130 5.06	3.3-18.7	**	9.2 (6.6) 108 0.33 (0.43)	-3.8 (-3.2)	-53.5 (-57.4) 6.6	5.4
	( NU=P+R )		8	(0.1-291.7)				(0.4-22.4)	(**)
	p-level: 1	***			***					***		***	***	0.39
	U vs.NU	(***)			(***)					(***)		(***)	(*)	(*)
	NU: Cement	26.0	29 8.7	10.0-127.1	39.9 (18.9)	29 7.50 8.8-154.4	0.41	45.8	22	0.52 (0.76)	-9.9 (-11.3)	-37.3 (-24.4) 24.1 24.1
	wells	(17.0)	0	(0.4-180.5)				(0.8-2073)	(0.64)	(36.5)
	NU:	2.6 (2.0) 77 5.3	0.7 -6.6	6.1 (4.9)	101 3.36	3.1 -9.9	**	6.1 (4.8)	86	0.28 (0.31)	-2.7 (-2.8)	-59.6 (-69.3) 0.0	0.0
	others		8	(0.1-69.7)				(0.6-65.9)	(*)
	1 :W1, W2 -samples of drinking water collected in the morning and evening, respectively; AM -arithmetic mean; GM -geometric mean; N -number of samples; SDg -geometric standard
	deviation. p-level: concentration (variance p, in parentheses). Notations for p-level, if explicit value is not given: p>=0.05 (n.s.), p<0.05 (*), p<0.01 (**), p<0.001 (***);

2 :

  TableIIPredicted Al intake with drinking water by 6-year-old children * Al intake was calculated at assumed average water consumption 1.5 L/day by 6-years-old children. PTWI (Al) = 7 mg (kg body weight) -1 week -1 , N -nember of households, GM -geometric mean; SDg -geometric standard deviation; 95 % confidence interval was calculated from geometric standard deviationTableIIIPredicted Al intake with drinking water by 'risk groups' of 6-year-old children *

					Food Additives and Contaminants	Page 38 of 39
							Deleted: V
	Section Section	Group Group	N N ‡		Al Intake Al Intake
			(category) (category)	(exceedan	[µg day -1 ] [ µg day -1 ]		% PTWI % PTWI	Deleted: microgram
				ce, %)	GM	GM	SDg Quartiles	95% c.i.
					(Median) GM	(Median GM(Media	SDg Quartiles	95% c.i.
	A				Morning water samples -W1 (Median) n
	C		Criteria: the current limit: Al W2 >200 µg/L
		1 Urban	84	66.1 (78.7) 0.28 (0.33)	2.4	0.21-0.43 0.05-1.51
		with children Non-urban: 1 Households	7 (4.5%)	554 (475)	2.52 (1.76)	1.9	1.62-4.40 0.69-9.23
		2 Peripheral 2 All	35 26 (6.9%) 534(438) 4.5 (4.9)	0.02 (0.02) 2.30 (1.79)	5.9 1.8	0.01-0.06 0.00-0.61 1.54-2.95 0.71-7.39
		3 Rural households &	32	18.1 (17.2) 0.07 (0.07)	8.6	0.02-0.29 0.00-5.13
		Non-urban: 3 Households:	19( 7.7%) 424(376)	1.84(1.75)	1.4	1.43-1.89 0.95-3.53
	B	4 Cement wells 5 Other sources 1 Urban Cement wells † Urban Non-urban; F 17 50 84 (24.1%) r 40.9 (22.4) 0.16 (0.11) 5.2 (5.1) 0.02 (0.02) Evening water samples -W2 9.5 5.8 91.8(106.1) 0.38 (0.45) 2.2 CW: 7 o (5.4%); 4 Households: NU: 7 998(793) 4.21(4.40) 2.1	0.05-0.81 0.00-13.51 0.01-0.07 0.00-0.68 0.27-0.59 0.08-1.77 2.29-8.76 0.97-18.24
	D	Non-urban: 2 Peripheral 3 Rural Non-urban: 4 Cement wells 5 Other sources Criteria: proposed new limit: Al W2 >100 µg/L -a simulation 35 13.5 (9.2) 0.06 (0.04) 3.3 38 20.9 (11.9) 0.09 (0.05) 5.2 17 55.2(27.6) 0.22 (0.13) 7.13 0.05-1.01 0.00-10.34 0.02-0.09 0.01-0.60 0.03-0.20 0.00-2.26 56 11.8(8.5) 0.05 (0.03) 2.95 0.02-0.08 0.01-0.42 1 Households 23 (15%) 254 (199) 1.10(0.83) 2.0 0.74-1.62 0.29-4.15 P with children e 2 All households & 88 (23%) 255(200) 1.08(0.85) 1.8 0.72-1.43 0.33-3.57 e Urban r 3 Households: 76(31%) # 229(186) 0.97(0.80) 1.6 0.69-1.25 0.41-2.31
		Non-urban; Cement wells † 4 Households:	10(34%) CW: 9%); NU: 12 (	506(511)	e i v e R 2.13(1.81) 2.7 0.80-4.78 0.30-15.40
							w
							O n l
							y

* http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk * The 'risk' group was selected following the water Al exceedance at criteria given in line C and D.

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants

Acknowledgements

The author wishes to acknowledge Maria Folta for laboratory and analytical assistance and to Professor Dr. hab. Zofia Zachwieja for interest of this study and comments.


THE FATE OF TRICHOTHECENE MYCOTOXINS DURING THE PROCESSING: MILLING AND BAKING

Keywords: Bakery products, Bread, Cereals

Toxic secondary metabolites produced by fungi representing Fusarium genus are common contaminants in cereals worldwide. To estimate the dietary intake of these trichothecene mycotoxins, information on their fate during cereal processing is needed. Up-to-date techniques such as HPLC coupled with tandem mass spectrometry (LC-MS/MS), was used for the analysis of seven trichothecenes (deoxynivalenol, nivalenol, HT-2 toxin, T-2 toxin, 15and 3-acetyldeoxynivalenol and fusarenon-X) in bread production chain (wheat grains, intermediate products collected during milling and baking process, breads). Regardless of whether the grains were naturally infected or artificially inoculated by Fusarium sp. in the field, the fractions obtained from the grain cleaning procedure contained the highest mycotoxin levels. During milling, the highest concentrations of DON were found in the bran, the lowest in the reduction flours. Baking at 210ºC for 14 min had no significant effect on DON levels. The rheological properties of dough measured by fermentograph, maturograph, oven rise recorder and laboratory baking test were carried out and based on obtained results the influence of mycotoxin content on rheological behaviour was investigated.

INTRODUCTION

Trichothecene mycotoxins, frequently occurring in cereal grains and, consequently, in cerealbased products, are secondary metabolites produced mainly by Fusarium ear blight pathogens, which are widely distributed in the temperate zone of all continents (Weidenbörner 2001[START_REF] Creppy | Update of survey, regulation and toxic effect of mycotoxins in Europe[END_REF], D'Mello et al. 1997). The incidence of these mycotoxins in food crops can vary considerably from year-to-year, depending on many factors; weather conditions and agricultural practices being the most significant [START_REF] Edwards | Influence of agricultural practices on fusarium infection of cereals and subsequent contamination of grain by trichothecene mycotoxins[END_REF]). Since these toxins can never be completely removed by processing [START_REF] Kuiper-Goodman | Risk assessment and risk management of mycotoxins in food[END_REF], preventive measures should be adopted at all stages of the production chain (i.e. growing in field, harvesting and storage to prevent contamination of final food products.

Trichothecenes are highly toxic chemicals acting both at cellular and sub-cellular levels.

Typical symptoms of Fusarium mycotoxicoses resulting from the consumption of seriously contaminated cereals include vomiting, dermal irritation, haemorragic lesions, depression of immune systems and weight reduction [START_REF] Schlatter | Toxicity data relevant for hazard characterization[END_REF]. In addition to health problems and/or economic losses, trichothecenes, when present in raw materials, may have negative effects on some technological processes such as during malting, brewing, fermentation and/or baking.

The physico-chemical nature of these phenomena have not been fully established yet [START_REF] Edwards | Influence of agricultural practices on fusarium infection of cereals and subsequent contamination of grain by trichothecene mycotoxins[END_REF]. To protect consumers from unacceptably high dietary intake of trichothecenes, maximum levels have been set in EU legislation (Commission regulation No 1881[START_REF]Commision Regulation (EC) No 1881/2006 of 19[END_REF][START_REF]Commision Regulation (EC) No 1881/2006 of 19[END_REF].

Currently, maximum levels are regulated in cereals (1250 µg/kg), flours (750 µg/kg) and bread (500 µg/kg) for deoxynivalenol (DON) only. Nevertheless, regulation for another two toxic trichothecenes, HT-2 toxin (HT-2) and T-2 toxin (T-2), is under preparation.

The fate of DON, the most frequent Fusarium toxin during various processing practices has been documented in several studies. Experiments concerned with distribution of DON among the milling fractions in naturally Fusarium contaminated wheat showed that the highest concentration of this mycotoxin is in the bran layer while in white flour, compared to whole grains, is reasonably lower [START_REF] Tanaka | Residues of Fusarium mycotoxins, nivalenol, deoxynivalenol and zearalenone in wheat and processed food after milling and baking[END_REF][START_REF] Abbas | Effect of cleaning, milling and baking on deoxynivalenol in wheat[END_REF]. In another milling study [START_REF] Trigo-Stockli | Distribution of deoxynivalenol and zearalenone in milled fractions of wheat[END_REF], the white flour had approximately half the level of DON in the cleaned wheat, whilst the bran contained levels two or more times higher than the wheat.

However, the effects of yeasts on DON levels in dough during the baking fermentation step are rather contradictory. [START_REF] Young | Effect of milling and baking on deoxynivalenol (vomitoxin) content of eastern candadian wheats[END_REF] observed an increase of DON in yeasted products while on the other hand, a mean reduction of DON in fermented dough of over 20% was documented [START_REF] Neira | The effects of bakery processing of natural deoxynivalenol contamination[END_REF]. The influence of various ingredients added to dough before baking on the DON levels in bread was studied by Boyaciouglu et al. (1993). Compared to control samples, a reduction of DON as high as 40% occurred in bread due to the addition of sodium bisulphite, L-cysteine and ammonium phosphate in to the dough. However, other additives such as potassium bromate and L-ascorbic acid were ineffective in this context.

In addition to health risks associated with the presence of trichothecenes in the food supply, other negative aspects have to be considered. The relationship between rheological properties of dough and extent of Fusarium infection in wheat has been discussed in the study of Dexter (1996). Lower water absorption of flour prepared from Fusarium infected grains, shorter development time and lower stability of dough, which also became progressively sticker and difficult to handle during sheeting and moulding, were described.

The objective of the present study was to evaluate the effects of wheat processing, milling and baking, on the transfer of mycotoxins into products such as flour and fermented bread. As in the case of other contaminants, the knowledge of the fate of mycotoxins during processing is important both for dietary intake estimation and adopting measures for its minimisation. In addition to these aspects, the effect of mycotoxin contamination on the dough rheological properties was determined.

MATERIALS AND METHODS

Samples

Wheat grains

Four grain samples (each 5000 g) obtained from wheat grown under well characterised conditions (Table I) in the year 2005 were supplied by Agricultural Research Institute in Kromeriz, the Czech Republic. Three of these samples (A, B, C) were naturally infected with Fusarium species; sample (D) was spray-inoculated in the field with a spore-mixture of Fusarium culmorum and F. graminearum (1:1; 10 6 conidia/ml suspension; 250-300 l/ha) at the time of flowering. The levels of trichothecenes in the grains used for the processing experiments were determined by LC/MS-MS and shown in Table I. were cleaned (sieving, scouring and polishing). During grains cleaning, screenings, the waste obtained by sieving process (the size of sieve was 2.2 mm; the laboratory screening machine in-house made prototype) and outer layers of bran, the combined waste from the scouring and polishing (the laboratory scouring in-house made prototype; the laboratory aspirator of dust particles Labofix Brabender) were collected. Milling fractions (three break flours fractions, three reduction flours fractions and bran fractions), according to the flow chart (Figure 1), were examined for their content of trichothecenes. The sizes of used sieves are shown in Figure 1. Weight proportions of wheat fractions and their moistures are reported in Table II.

Bread

The process of bread making is outlined in Figure 2. The main components of white flour were prepared by the mixing of all three break flours and the 1st reduction flours obtained from grains A -D according to schema in Figure 1 (for weight see Table II). For the preparation of one batch of breads, 300 g of white flour was taken and the following ingredients were added: yeast Saccharomyces cerevisiae (12 g), saccharose (5.1 g), vegetable oil (4.5 g), salt (2.1 g), distilled water (150 -165 ml; with the volume varying according to bonding power). Immediately after kneading the dough was placed in a covered bin (container) containing a laboratory thermostat and allowed to ferment at 30°C for 50 minutes.

The dough was then divided into pieces of approx. 70 g each and moulded by hand. The final proof of the dough pieces proceeded at the same temperature (30°C) as during the stage of fermentation for 45 min. The loaves of bread were baked in an electric laboratory oven at 240°C for 14 min. All intermediate products prepared within the baking process were dried for 24 hour at 40ºC prior to analysis.

Standards

Trichothecene standards (deoxynivalenol, DON; nivalenol NIV; HT-2 toxin, HT-2; T-2 toxin, T-2; 15-and 3-acetyldeoxynivalenol, ADONs and fusarenon-X, Fus-X) were purchased from Sigma-Aldrich (Germany) and Biopure (Austria). Certified reference materials, "DON in wheat flour" (< 0.05 mg/kg, BCR 396, Belgium) and "DON in naturally contaminated wheat" (0.7 ± 0.1 mg/kg, R-Biopharm Rhone, UK), were used as controls to ensure the accuracy of measurements. The values obtained for DON were in the uncertainly range of certificate concentrations. Representative samples (12.5 g) was extracted with 50 ml acetonitrile:water mixture (84:16,v/v) for one hour using an automatic shaker (IKA Laboratortechnik, Germany). Crude extracts were then filtered (Filtrak No. 390, VEB Freiberger, Germany) and 8 ml aliquots transferred into sample tubes to which 80 µl of acetic acid (99%, Sigma-Aldrich) were added.

Extraction and clean-up procedure

Purification was achieved by solid phase extraction (SPE) employing the MycoSep TM 226 cartridges (Romer, Austria). Four ml of purified extract were evaporated to dryness and the residues transferred into 1 ml of a water:methanol mixture (80:20, v/v) and passed through a 0.2 µm microfilter (Alltech, USA) prior to further analysis.

Chromatographic conditions

High performance liquid chromatography (HP1100 Binary Series LC system, Agilent Technologies, USA) coupled with a mass spectrometer (Finnigan LCQ Deca, USA) were used for the analysis of purified extracts. Chromatographic separation of sample components was carried out on a reverse phase column with polar endcapping (Synergi Hydro RP, 150mm

x 3mm x 4µm, Phenomenex, USA) heated at 40 ºC and operated under gradient conditions.

The mobile phase was composed of 10 mM amonium acetate in purified water (A) and methanol (B). The flow rate of mobile phase was set to 0.5 ml/min and the injection volume was 20 µl. Gradient elution was performed starting from A:B (80:20, v/v) and reaching A:B (30:70, v/v) in 8 min. From 8 to 15 min the ratio A:B (30:70, v/v) was stable and then jumped to A:B (80:20, v:v). The time of post run lasted 7min.

Identification and quantification of analytes was performed using MS/MS with the following parameters (ion source type -APCI operated both in negative and positive ion modes, capillary temperature -150 °C, vaporizer temperature -450 °C, nitrogen sheath gas flow -1.2 l/min, nitrogen auxiliary gas flow -3 l/min, source voltage -6 kV, collision gas -helium, scan type -selected reaction monitoring). APCI ionization modes ( + / -) and monitored Performance characteristics of analytical method / Quality assurance Limits of detection (LODs) and quantification (LOQs), recoveries and repeatabilities expressed as relative standard deviations (RSDs) that were obtained within the validation process are reported in Table III. Calibration curves for all analytes were linear within the working range from 5 to 10 000 µg/kg. Squared correlation coefficients (R 2 ) were in the range 0.9991 -0.9999 for 11 point calibration curves.

The analytical method used for samples examination was accredited (ISO 17025) for cereals; as a part of external quality control the trueness of generated data was demonstrated through participation in Food Analysis Performance Assessment Scheme (FAPAS) organized by Central Science Laboratory (CSL, York UK). The z-scores for all analysis (DON, ZON, HT-2 and T-2) were in the range ±2.

Moisture determination

Moisture was determined gravimetrically by drying samples in an oven for 2 hours at 131 ± 2 ºC according to ISO Standard No. 712 "Moisture determination in cereals and cereal products".

RESULTS AND DISCUSSION

In common with some other contaminants, the levels of mycotoxins in processed foods may differ considerably from those in raw materials. Depending on the household and/or industrial processing practise, both reduction and accumulation of toxins may occur in a particular

Distribution of Fusarium mycotoxins in wheat grains

For processing experiments, four wheat grain samples, representing various contamination characteristics (both in terms of total content of trichothecenes and their patterns) were used.

A fairly uneven distribution of trichothecenes within the fractions obtained during the milling process was apparent and is shown in Table IV. Relatively very high contents of trichothecenes were found in waste fractions obtained within cleaning process. Effective removing of all screenings and outer layers of bran from the surface of grains during cleaning steps reduced DON content by 50, 55, 41 and 47% in cleaned wheat samples A, B, C and D, respectively. The contents of DON in individual milling fractions are shown in Figure 3.

Considering the weight contribution of various flour fractions to the total mass of cleaned grains taken for milling, most of DON (19,17,21 and 20% in A,B,C and D samples,respectively) was contained in the 1st reduction flours because of high DON levels and amount of this type of milling fraction. Very similar results were reported by Japanese researchers [START_REF] Tanaka | Residues of Fusarium mycotoxins, nivalenol, deoxynivalenol and zearalenone in wheat and processed food after milling and baking[END_REF]) who used the same type of laboratory mill for fractionation of naturally contaminated wheat. Generally, the data obtained by different mill might not be comparable. V. Considering the total amount of DON contained in white flours which were taken for breads production, its levels were higher by 21 -40% in kneaded dough, however during fermentation, a significant decrease occurred of approximately 38 -46% of the original content (the calculation, i.e. basis for flour amount in the same in all these products). Surprisingly, the dough pieces at the end of proofing phase contained relatively high DON levels, ranging from 132-145% of the total flour content in products A, B, C and D. However, when comparing the content of DON in the raw materials (flours) and final products (loaves), practically no changes occurred with a maximum loss of 6% in bread relevant to sample D. In spite of apparently unaffected DON levels in the dough processing is accompanied with distinct fluctuation of its contents. Release of DON from conjugated form probably is followed by its transformation into a substance that cannot be determined by conventional analysis, unfortunately, its nature remains unclear. It is worth noting that the same profile of changes in DON levels occurred in all samples regardless of the extent of contamination. Remarkably a temporary drop of DON (approximately 40%) as the result of dough fermentation was also observed in a reference earlier studies. Overall, the DON content in loaves was practically the same as that determined in white flours taken for their preparation. In other words, no mitigation of this mycotoxin occurred during bread-making process. This conclusion complies with results obtained in several studies carried out in various countries, see Table VI. Recently, many discussions on masked mycotoxins in cereals have been opened. Deoxynivalenol-3-glucoside has been shown to be the most abundant DON derivative [START_REF] Berthiller | Masked Mycotoxins: Determination of a Deoxynivalenol Glucoside in Artificially and Narturally Contaminated Wheat by Liquid Chromatography-Tandem Mass Spectrometry[END_REF]. Unfortunately, it was beyond the scope of this study to search for DON conjugates. In any case, the efficiency of extraction process was tested for each baking intermediates. Hence it seems unprovably that drops of DON content were due to its bounding by the yeast. VII.

Changes of DON concentration during baking process

Maturograph characteristics belong to the most important parameter in description of rheological dough properties. Proofing time ensures the optimum time for reaching the maximum volume of final bakery products. It was clear, that dough prepared from infected grains needed a longer period for approaching this optimal time. Dough resistance against mechanical stress during proofing correlated with DON content with the higher contamination in dough giving the higher dough resistance. On the other hand, the elasticity of fermented dough was not affected by extent of contamination. Proofing stability of dough reflects the time tolerance of optimal proofing and so ensures the highest volume of the final products.

Regarding this parameter, the dough stability prepared from the sample contaminated with the lower amount of DON (Sample C) was twice as high in comparison with the flour contaminated with the most DON (Sample D).

The values of baking volume recorded by oven rise recorder, which indicates the final volume of baked products, were slightly higher for samples containing mycotoxins. This trend was found also for another rheological factor, oven rise that characterises difference in volume between baked product and unprocessed dough.

CONCLUSIONS

The results obtained within this study can be summarised as follows:

i)

The highest levels of both type B trichothecenes (deoxynivalenol, nivalenol, sum acetyl-DON) and type A trichothecenes (HT-2 and T-2 toxin) were concentrated in waste fractions (screenings, outer layers of bran) obtained during cleaning of wheat grains. On this occasion, a potential health risk for staff breathing these materials at gristmills should be mentioned.

ii) A substantial part of DON in cleaned grains is located in the bran. Nevertheless, approximately 40% of its original content was still left in flour fractions; the 1st break flour was the most contaminated.

iii) Pronounced changes of DON levels in dough samples taken in particular steps of baking process might be due to transformation of DON forms (free / conjugate). More research is needed to explain these phenomena.

iv) No significant reduction of free DON levels occurred as the result of bread baking process (210ºC, 14 min).

v) Some rheological properties such as proofing time and dough stability were found to be worse in samples prepared from more DON contaminated grains, nevertheless more than DON level, the overall, impact of the mould growth on the rheological properties is important.

F

  an automatic laboratory mill (model MLU-202, Bühler), grain samples

  To describe visco-elastic behaviour of dough during kneading, fermentation and proofing, the determination of dough quality was carried out by rheological instruments, such as fermentograph (SJA, Sweden), maturograph (Brabender, Germany), oven rise recorder (Brabender, Germany) and laboratory oven (Zaklad Badawczy Przemyslu Piekarskiego, Poland). Fermentograph monitors the first stage of dough proofing, called fermentation, by measuring dough volume and fermentation gasses. Maturograph is used to record the fermentation process during the proofing time by means of a sensing probe which touches the dough surface. The oven rise recorder describes the changes in volume of dough during the first stage of baking in an oil bath in a temperature range from 25°C to 100°C, when changes of proteins and starch caused by their denaturation are occurring in the dough during baking. Ttrichothecene analysis LC/MS/MS) was used for the analysis of 7 trichothecene mycotoxins in sample extracts purified by solid phase extraction (SPE). Wheat grains (raw materials), milling fractions and intermediates collected during baking as well as the resulting breads.

  z (parent ion>daughter ion, confirmation ion) used for individual analytes were DON -(371>311, 281), NIV -(355>295, 265), FUS-X -(413>353, 263), ADONs -(397>337, 307), HT-2 + (442>425, 263) and T-2 + (317>273, 299).

  any case, these changes should be taken into account when estimating consumer exposure on the basic of data obtained by analysis of cereal grains.

Four

  Figure 1), were used for baking experiments. DON levels in these flours corresponding to

  content on rheological propertiesAs mentioned earlier, besides of health risk associated with infection of cereals by Fusarium fungi, other problems corresponding to the quality of flour may be encountered. Two important fermentographic characteristics, dough volume and time of fermentation, describe properties and behaviour of dough before the collapse of its structure caused by biochemical changes within fermentation. The values of the two fermentgraphic factors mentioned above in wheat samples A, B, D were higher approximately by 40% than in sample C (the lowest DON level), see Table

Figure 3

 3 Figure 3 DON distribution (%) in milling fractions (total DON amount in of 4000 g cleaned grains = 100%; the contributions were calculated on the weight of obtained milling fractions shown in TableII) and determined concentration levels of trichothecenes in these samples summarised in TableI

  Figures 4 -7 DON levels in intermediate products collected during baking process (white flour = F; kneaded dough = KD; fermented dough = FD; dough after final proof = DP; bread = B), concentrations expressed on the dry weight of respective product

  Thanks for providing valuable advices and carrying out the milling experiments belong to Oldrich Famera, MSc. from the Czech University of Life Sciences Prague and the bakery experiments belong to Assoc. Prof. Marie Hrusková from the Department of Carbohydrate Chemistry and Technology (ICT Prague).

	o r o r F o r F o r Figure 1 Simplified chart of milling process used for obtaining experimental samples (W = 44W; 363µm 120SS; 145µm 44W; 363µm 120SS; 145µm 64W; 275µm 120SS; 145µm 1st BREAK FLOUR 2nd BREAK FLOUR 3rd BREAK FLOUR BRAN CLEANED WHEAT BREAK ROLLS SIEVES 44W; 363µm 120SS; 145µm 44W; 363µm 120SS; 145µm 64W; 275µm 120SS; 145µm 1st BREAK FLOUR 2nd BREAK FLOUR 3rd BREAK FLOUR BRAN CLEANED WHEAT BREAK ROLLS SIEVES F r o Republic. F wire; SS = stainless steel) Figure 2 Bread making process
	REDUCING ROLLS SIEVES REDUCING ROLLS SIEVES	P e e r P e e r P e e r P e e r 54W; 316µm 120SS; 145µm 72W; 243µm 120SS; 145µm 54W; 316µm 120SS; 145µm 72W; 243µm 120SS; 145µm P e e r	-120SS; 145µm -120SS; 145µm	SHORTS SHORTS
	1st REDUCTION FLOUR 1st REDUCTION FLOUR	R R R R 2nd REDUCTION FLOUR 2nd REDUCTION FLOUR R	3rd REDUCTION FLOUR 3rd REDUCTION FLOUR
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			v i e v i e v i e v i e v i e
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Table I

 I Characteristics of wheat grains used for processing experimentsTable III Validation data obtained by LC-MS/MS method (n=5)

									Deleted: V
	Parameter	Matrix	DON	NIV	Fus-X	ADONs	HT-2	T-2
	LOD (µg/kg)	wheat	0.5	1	1	1	5	0.5
	Sample Code LOQ (µg/kg) A	Wheat cultivar dough bread Sulamit wheat dough	Preceding crop 5 maize 87.6 ± 3.6 53.3 ± 3.5 83.1 ± 4.5 Fusarium infection Way of cultivation 10 10 10 natural conventional 82.8 ± 5.9 89.1 ± 3.1 42.8 ± 9.7 80.8 ± 7.1 84.0 ± 6.5	Trichothecene levels (µg/kg) DON NIV ADONs Fus-X HT-2 10 5 909 23 35 < 1 < 10 95.4 ± 3.2 83.5 ± 4.7 85.9 ± 6.0 91.2 ± 4.0	T-2 < 5
	B C D Recovery *± RSD (%)	Sulamit Ebi Ebi fermented dough proof dough after	trifolium rape maize 87.4 ± 4.1 46.7 ± 6.7 83.8 ± 4.9 natural organic natural conventional artificial conventional 2985 108 92 85.2 ± 5.4 88.2 ± 5.4 81 30 < 10 87.4 ± 4.1 46.7 ± 6.7 83.8 ± 4.9 85.2 ± 5.4 88.2 ± 5.4	< 10 < 10 69 92.0 ± 3.8 92.0 ± 3.8	< 1 < 1 < 1	22 < 5 < 10	< 5 < 5 < 5
		bread	86.4±2.7	43.8±5.2	85.1± 5.6	86.3 ± 6.0	86.1 ± 3.9	89.0 ± 4.1
	*analytes' levels in spiked samples were 480 µg/kg
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  Table IV Distribution of trichothecene mycotoxins in milling fractionsTable VII Rheological characteristics of fermented dough MaU -maturographic unit; OrU -oven rise recorder unit; V D -dough volume; T FER -fermentation time; T MAT -proofing time; R D -dough resistence; S MAT -proofing stability; V B -bread volumeTable VI DON transfer from contaminated flour to bread (reported in earlier published studies)

	Sample Code (see Table I) Mycotoxin content (µg/kg dry weight) Oven rise recorder B V D (FeU) T FER (min) T MAT (min) R D (MaU) S MAT (min) V B (OU) A Fermentograph Maturograph Oven rise (OrU) 91 60 40 820 6 620 190 83 57 46 725 8 490 190 61 44 34 715 12 480 195 85 59 40 880 6 595 220 Sample characteristics Sample Code A B C D Product / Process conditions DON in flour (µg/kg) Remaining DON in bakery product (% of flour)* Study naturally infected wheat non-yeasted sponge 170 ºC for 30 min 193 105 Tanaka et al., 1986 FeU -fermentographic unit; Deleted: IX C spiked flour (500 µg/kg) non-yeasted sponge 170 ºC for 30 min 665 101 Tanaka et al., 1986		D
	Product uncleaned grain artifical infected wheat NIV 23 heads	DON ADONs 909 35 yeasted brad HT-2 < LOQ < LOQ T-2 220 ºC for 20 min	NIV 81 3130	DON ADONs HT-2 108 < LOQ 29 93	T-2 < LOQ Boyacioglu et al., NIV 30 1993	DON ADONs HT-2 92 < LOQ < LOD < LOD T-2	NIV < LOQ	DON 2985	ADONs HT-2 69 < LOQ < LOQ T-2
	impurity dust naturally infected wheat 19 36 cleaned grain < LOD naturally infected wheat	1665 7175 210 ºC for 10 to 40 min 43 173 94 145 yeasted brad 456 < LOD < LOQ < LOD < LOD 26 44 9 245 500 -1000 yeasted brad 205 ºC for 30 min 4100	705 2689 59	< LOQ 49 24 -100 < LOD < LOQ < LOQ 23 < LOQ 226 36 Neira et al., 1997 88 42 16 107 Scott et al., 1984	105 671 38	43 43 < LOD < LOD < LOD < LOD < LOD 21 < LOD	22 69 < LOD	8419 25020 1413	139 425 12	32 37 < LOD < LOD 11 < LOD
	1st break flour 2nd break flour naturally infected wheat < LOD < LOD	391 278	< LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD Yeated brad data not available 520.9	64 42	< LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD 31 -81 Abbas et al., 1985	42 34	< LOD < LOD < LOD < LOD 8 < LOD	< LOD < LOD	1057 827	< LOD < LOD < LOD < LOD < LOD < LOD
	3rd break flour 1st reduction flour 2nd reduction flour < LOD < LOD < LOD 3rd reduction flour < LOD bran < LOD F 270 314 202 170 975 o < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD 78 < LOD < LOD r r o F *DON in flour (100%)	49 32 26 33 117	< LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD 93 77 62	39 27 18 23 90	< LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD 21 < LOQ < LOD < LOD < LOD	< LOD < LOD < LOD < LOD < LOD	786 764 455 396 2451	< LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD 32 < LOD < LOD
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ACKNOWLEDGEMENTS

This research was supported by the National Agency of Agricultural Research (NAZV) of the Czech Republic, project no. QF3121 (1Q57042). Part of funding was obtained from project MSM 6046137305 granted by the Ministry of Education, Youth and Sports of the Czech