Pesticide residues in the po river watershed: Application of a mathematical model

Agriculture, Ecosystems and Environment, 27 (1989) 539-553

539

Elsevier Science Publishers B.V., Amsterdam - - Printed in The Netherlands

P e s t i c i d e R e s i d u e s in the P o R i v e r Watershed: A p p l i c a t i o n of a M a t h e m a t i c a l M o d e l A.A.M. DEL RE l, D. COVA2, L. RAGOZZA1, E. RONDELLI 1, L. ROSSINI 2 and M. TREVISAN 1

~Istituto di Chimica, Facolt~t di Agraria, Universit~ Cattolica del Sacro Cuore, Piacenza (Italy) eDipartimento di Farmacologia, Centro C.N.R. Farmacologia Infrastrutture CeUulari, Universit~ di Milano (Italy) (Accepted for publication 19 April 1989 )

ABSTRACT Del Re, A.A.M., Cova, D., Ragozza, L., Rondelli, E., Rossini, L. and Trevisan, M., 1989. Pesticide residues in the Po River watershed: application of a mathematical model. Agric. Ecosystems Environ., 27: 539-553. A mathematical model, Mackay's fugacity model, has been applied to study the distribution of various pesticides in the Po River watershed and other North-Italian watersheds. The fugacity model has been used at the first level, i.e. at equilibrium among compartments, and modified by introducing both a 'plant biomass' and a 'men' compartment. The model was applied to a number of pesticides. Volumes of air, water, soil, sediment, biomass etc. were estimated for a whole watershed. The physico-chemical characters of the pesticides and the amounts used have been used to calculate fugacity capacities. Predicted concentrations in the various compartments were compared with residue level data from the ResPest-I database.

INTRODUCTION

A follow-up of the chemical compounds in the environment is necessary for the assessment of risks to human health. Man's exposure to chemical compounds can be evaluated only by determining the nature and number of compounds introduced into the environment, their metabolic pathways, half-lives and the amounts which are introduced into the organism by foodstuffs, air and water. The quantitative evaluation of a chemical compound in the environment is difficult and expensive, partly because of the number of possible contaminants, and partly because in some instances its concentration is so low that it cannot be detected. The use of mathematical models of environmental distribution has grown over the last decade. With these models it is possible to predict the compart-

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© 1989 Elsevier Science Publishers B.V.

540

mental distribution of various substances, to set out convenient defence mechanisms, to select the less dangerous amounts of new substances, and so on (Neely, 1980; Paterson, 1985). It is possible to select various models depending on the behavior of the compound studied and on the available data. The differentiation and selection of a convenient model is one of the problems encountered. Static models, like the one presented here, do not take into consideration transport mechanisms or chemical reactions of the compounds studied. Since they are simple and do not rely on empirical assessments of kinetic parameters, they can be very useful. However, their predictiveness is limited to general indications of relationships between the amounts of the chemical compounds in the various environmental compartments and their concentrations (Mackay and Paterson, 1982; Paterson, 1985). The most important parameters in a static model are, on the one hand, the nature, number, volumes and characteristics of the compartments examined, and on the other hand, the need for careful screening of partition-coefficient evaluation methods. In this paper, the effects on the predicted concentrations and their ratios to the compartments considered will be discussed on the basis of the calculation methods of Mackay and Paterson (1981, 1982) and of a single set of partition coefficients. It is important to compare model predictions with experimental data; therefore, the average estimated concentration of pesticides in northern Italy, obtained through ResPest-I (Trevisan, 1987 ) data bank, were used. Eleven pesticides were compared, particularly atrazine and DDT, because of the great diversities in their chemical characteristics and in their use. For our purposes, two compartments were added to the basic model of Mackay and Paterson (1981, 1982): plant biomass (Vighi et al., 1987; Calamari et al., 1987 ) and man biomass. The latter, as described in this paper, was introduced to predict in a direct mode the possible pesticide amounts and concentrations in man, within the range of the static model selected, comparing them with the available experimental data. The amounts and concentrations obtained in this way are linked to the effects on man caused by pesticides and other environmental contaminants. METHODS

Physical models To achieve such comparison, different static models, as described in the literature (Frische et al., 1979; McCall et al., 1983; Paterson, 1985) and shown in Table 1, have been used. Both 'Padania' models are based on MackayPaterson's model (1981, 1982). The original model employs the 'Unit World' as an area of 1 km 2, with:

541 TABLE 1 Compartment volumes (m 3) in some environment models 1 Compartment

Ecosystem K15pffer O.E.C.D. Terrestrial Rural

(P6)

(P8)

Air Water Soil Sediments Suspended matter Aquatic biota Men Plants

1.0,10 l° 3.0,108 5.4,104 1.5,104 1.5,101 3.0

6.8,1014 2.4,10 TM 2.6,10 TM 5.7,107 1.5,107 7.3 * 105

6.8,10 TM 2.4,1012 2.6,101° 5.7,107 1.5,107 7.3 * 105 1.6,106 3.6,109

1.0,104 1.0,101 1.0 7.0,104 2.3,101 3.5

6.0,109 7.0,108 3.0,106 5.0,103 1.0 0.1

1.9,104 1.9,104 3.8,104

6.0,109 1.0,105 1.3,105

~References: Ecosystem (MacCall et al., 1983); K15pffer (Frische et al., 1979); O.E.C.D. (Paterson, 1985); Terrestrial (MacCall et al., 1983); Rural (Paterson, 1985); (P6) and (P8): the 'Padania' model with 6 and 8 compartments (this paper).

(1) air, 6 km high, density 1.21 kg m-3; (2) water, 70% of the area, average depth 10 m, density 1000 kg m-3; (3) soil, 30% of the area, depth 15 cm, density 1000 kg m -3, organic carbon 2%; (4) sediments, 3-cm deep layers under the water surface, density 1500 kg m-3, organic carbon 4%; (5) suspended matter in water, concentration 5 cm ~ m -3 of water, density 1500 kg m -3, organic carbon 4%; (6) aquatic biota, assumed to be fish, volume ratio of 1 cm 3 m -3 of water, density 1000 kg m -3. Such assumptions have been employed with a few modifications for the 'Padania' models developed in this paper (Table 2). Partition coefficients

Formulae shown in Figs. 1 and 2 (Mackay and Paterson, 1981) have been used for estimating partition coefficients. The two 'Padania' models

Two 'Padania' models have been developed, one with 6 compartments and one with 8 compartments. The former is similar to the reference model (Mackay and Paterson, 1981 ), whereas the latter differs because of the introduction of two new compartments, 'plant biomass' and 'men'. Both models include the North-Italian watersheds of the Po River, the Adige, the Brenta, the Reno, and other watersheds in Romagna, as well as the watersheds of the upper Adriatic side, with the rivers Piave, Tagliamento, and Isonzo. We define this area as the 'Po-and-Adige' watershed. It includes, almost completely, Valle d'Aosta, Piemonte, Lombardia, Trentino-Alto Adige, Veneto, Friuli-Venezia Giulia, and Emilia-Romagna (ISTAT, 1984a).

542 TABLE 2 The 'Padania' models: areas, volumes and densities of the compartments. ' M e n ' and ' P l a n t s ' are not included in the 6 - c o m p a r t m e n t model Air

Water Fresh water volume Groundwater Sea water

Total Soil M o u n t a i n fields

Hills

Flat lands

Total Sediments Suspended solids Aquatic biota

Area Depth Volume Density

Volume Area Depth Volume Volume Density Area Depth Volume Area Depth Volume Area Depth Volume Volume Density Volume Density Volume Density Volume Density

Men Weight Volume Density Plants Volume Density

116742 k m 2 (land) + 18844 k m 2 (Adriatic sea, n o r t h ) 5 km 677930 k m 3 0.001205 kg l 118 k m 3 (lakes) +46.2 k m 3 (other) 164.2 k m 31 18844 k m 2 (Adriatic sea, n o r t h ) 30 m 565.3 k m 3 2371.5 k m a 1 kg l - 1 54389 k m 2 10 cm 5439 k m 3 20683 k m 2 30 cm (south of Po river) or 25 cm (other) 5463 km 3 41852 km 2 45 cm (south of Po river) or 40 cm (other) 15482 km 3 26385 km 3 1.5 kg 1-1 0.057 k m a 1.5 kg 10.015 km ~ 1.5 kg 10.00073 k m 3 1 kg 123'892'993 [1981] 67.4 kg m a n 0.0016 km 3 1 kg 112.3 kg m -2 (dry weight, 50% water) 3.6 k m 3 0.8 kg 1-1

1 ( 10 * surface-water volume )

(Kiow)= 0 . 3 2 3 - 0 . 0 1 , m . p . - Log (Ki,,¢)=Log (Kiow)-0.21 (Kib) = 0 . 8 5 , L o g (Kiow)- 0 . 7 K'p=K~o¢, (% c.o./100). Log Log Log

(water solubility [ (moles 1-1 ] )/0.944

543 A substance i in 2 comparts (I a n d II): [ a activity; li chemical potential; R gas constant; T absolute temp] [.tiI = ~tiII = ~ix~ ~Lix = p.i*X + R • T • In (aix) + . . . ailI / aiI = KilI. A substance i in j comparts: [f fugacity; p partial v a p o r pressure; V c o m p a r t m e n t volume; n n u m b e r of moles; H H e n r y ' s constant] v: v a p o r p h a s e fi • Viv = niv • R • T ; ziv = niv • 1 / (fi • Viv) = 1 / (R • T) r: pure phase ~t'r=~t r + [ R • T • I n ( a i r ) ] a: aqueous phase (sat:saturated) ~tia=P i*a + R • T • I n ( a i a ) ~ ~ti*r + R • T * I n ( a i r ) aia / air = t1 / fisa t ; aia ~ nia / Via ; H i - t~sat / cir zla=nia/(Via• fi)=l/H i j: other comparts zij = Zij.a • zia Computation: n i = niv + nia + ~ nij niv = fi • Viv / (R • T); nia = fi • Via / Hi;

niv = fi • Viv • Ziv nia = fi • Via

•

Zia

nij = fi • Vij • Kij.a / Hi; nij = lq • Vii • Z~ = f i • v i j • k i j • zia then: fl = n i / (Viv • Ziv + Via • Zia + . . . + Vij • zij) n i = fi • Viv • Ziv ; and: Civ=f i •Ziv; Testing: f i • Viv • Z i v + f i • V i a • z i a + ~ C ~ • V ~ - n i = 0 ; t~ = Cia / Zia w h a t is known: cia; ...; 0j [from RESPEST-I] ziv ; zia [from t h e r m o d y n a m i c s ] w h a t is estimated: w h a t is u n k n o w n : ni; ff Viv; Via; ...; V~ C o m p u t i n g the k's: zij=clj/il; D j = z i j / z i v ; ... Fig. 1. C o m p u t a t i o n a n d t e s t i n g o f t h e m o d e l .

Both models include,besides the watersheds, their continuation toward the east in the northern Adriatic area up to the Dalmatian coasts, north of the straight line connecting Ancona with Cap• Promontore in Istria. Politicalborders, not watershed contours, have been used as boundaries to exploit census, statisticsof consumptions and data on pesticideresidues. The widest excluded area is the Canton Ticino. N o consequences are expected in

544

s: soil zib = d e n s i t y s •

Kip/Hi

se: sediments zise = densityse • Kip~H i sin: suspended matter

Zism= d e n s i t y s m

* Kip/H i

b: aquatic biota

zib=Kib/Hi m: men

average composition (I kg):

water fats proteins b o n e minerals other m i n e r a l s

624.0 g 153.1 g 164.0 g

47.7g 10.5 g

man 2 sub-compartments: zlm,1 = 1 / H i zim,2 = K i o w / H i

1 - water 2 - fats

zim, l + 2 = 0.624 / H i + 0.1531 • K i o w / H i

whole man

pl: plants Zipl = d e n s i t y p l • 10 ^ (5.943 - 2.385 * L o g (m.w.i)) F i g . 2. Formulae used for calculating the partition coefficients and the zetas.

results, as will be discussed later. Surfaces, volumes and densities of each compartment are shown in Table 2.

Soil compartment The territory has been divided according to altimetry into mountains, hills, and flat lands in accordance with the classification adopted by ISTAT (1984a). Mountains Although the average depth of organic substance in forested lands is 10-15 cm, many mountain lands have practically no soil covering. Other level grounds or hilly countries, though classified as mountains, have thicker layers of soil. An average thickness of 10 cm has been assumed for the active layer, i.e. the layer in equilibrium with the other compartments (Table 2). Hills The hills, estimated as 2.068.256 ha (ISTAT, 1984a), have been divided into two parts: the lower-Appennine area of Emilia-Romagna (585.572 ha), and the remaining area (1.482.684 ha), mainly the lower-Alp lands. The depth of the active layer has been assumed to be the same as the average depth of soil tilling: 30 cm for the clayey soils in Emilia-Romagna, and 25 cm elsewhere. The soil active volumes for both areas were 1.757 and 3.706 km 3, respectively (Table 2).

545 Flat lands As in the case of the hills, the flat lands of 4.167041 ha (ISTAT, 1984a) have been divided into two parts: those of Emilia-Romagna (897.816 ha, depth of the active layer: 45 cm), and the others (3.269.225 ha, 35-cm deep) (Table 2). Air

The height of the mixed zone of the air compartment, 10 km in the original model (Mackay and Paterson, 1981 ), has been reduced to 6 km by the same authors (Paterson, 1985). In the 'Padania' model, the height has been further reduced to 5 km, i.e. to the volume limited by the Alps chain, whose highest peaks reach almost 5 km in height (Mount Bianco 4810 m, Rosa 4633 m) (Table 2 ). Wa~r

The compartment includes rivers, lakes and ground waters present in the area considered, besides the above-mentioned Adriatic zone. Fresh water The big, northern lakes (Garda, Maggiore, Como, Iseo, Orta ) take up a water volume of 118 km 3 (S. Galassi, personal communication) on the whole. It is difficult to determine the exact average amount of water present in a region at a given time. As the problem is even more complex for rivers, not all water volumes of the Po River were considered. Water volume of the Po River has been assessed by means of the values of average rainfall in m m (D), average specific turbidity (T) in kg m-3, and (Bd) the area of the Po watershed drainage area down to the station of Pontelagoscuro (Ministero Lavori Pubblici, 1986) in km 2. The volume of river water (V~) has been calculated as (D.Bd), and corrected by subtracting the suspended matter, calculated as ( D . B d . T), see Table 2. Vaf= ( D . B d ) - ( D . B d . T)

The volume of the Po waters {46.2 km 2) is greater than the volume of water of the other rivers, and the total volume of fresh waters (164 km 3) is much less than the total volume of waters. As the actual volumes have very little influence on the reckoned concentration, the river water underestimation is negligible. Groundwater Groundwater has been assessed by a 10-fold correction factor (Odum, 1980) on the volume of surface fresh waters. This may lead to an overestimation of the ground water, as well as of the total water, in a system such as the 'Padania',

546

which could be compensated for by a possible underestimation of surface fresh waters (Table 2). Sea water

Maximum depth of the Adriatic is only 66 m, and the strip between the isobaths of 25 and 50 m (T.C.I., 1927) covers about 70% of the area. Therefore, a depth of 30 m has been assumed, which led to a volume of 565 km 3 (Table 2). Aquatic biota

The same ratio of water volume and biota (fish) as in Mackay's model ( 1981 ) has been used for the aquatic biota. Ground waters have been excluded from the calculations; only lake, river and sea waters have been included (Table 2). Sediments

As in Mackay's model (1981), it is assumed that the active layer of sediments is 3 cm on the bottom of all the surface and sea waters (Table 2). Suspended matter

The ratio of 5 cm 3 of suspended matter m - 3 of water, already used by Mackay (1981), has been used for the sediment suspended in lake and sea water. This results in a volume of 0.00346 km 3 of sediment suspended in (118 + 565 ) k m 3 of water. In the case of the river water, the previously assessed value D*Bd* T--0.0117 km 3 has been used (Table 2). Plants

The plant compartment has been tentatively introduced by Calamari et al. (1987). It is assumed that the weight of plants m -2 was 12.3 kg (dry weight) m -2 or 24.6 kg (wet weight) m -2, and that the average density of plants was 0.8 kg 1-1 (Whittaker, 1975). The total above-sea level area (11.674.192) has been counted as covered area (Table 2). Men

This compartment can be described as consisting of two sub-compartments: an aqueous one (62.4%) and a lipidic one (15.3%). The equilibrium quantities in both sub-compartments have been separately calculated for any pesticide (/-pesticide) as follows: ( 1 ) by the fugacity capacity zim.1= 1 / H i for the aqueous phase, where H i is the Henry's constant of an/-pesticide; (2) by the fugacity

547

capacity z ira.2= Kiow/H ~,where K~owis the octanol: water partition coefficient of an/-pesticide. In this paper, no empiric corrections concerning metabolisation have been made. The total volume of a men compartment has been assessed on the basis of a population of 23 893 000 inhabitants (1981 census (ISTAT, 1983 ) ) and on an average weight of 67.4 kg per inhabitant. The density has been set at 1 kg l- 1 (Table 2 ).

Pesticides The pesticides to be tested with the model (Table 3) have been chosen as follows: (1) at least two pesticides per activity class: fungicide; herbicide; insecticide; (2) some organochlorine compounds, in order to sample substances massively used in the past but presently not allowed by most regulations, peculiar for long persistence and high lipophilia, as shown by the extreme values of Kiow (Table 3); (3) chemicals with very different chemico-physical characters, namely volatility, solubility in water etc.; (4) chemicals widely screened in Italy and therefore present in the ResPest-I database, in order to compare the expectations of the model with the experimental data; (5) pesticides whose consumption in the 'Po-and-Adige' watershed are known.

Data from ResPest-I database The ResPest-I database was made by our Institute and contains all papers published from 1978 to date. Included are data on pesticide residues in Italy. The structure of the database is discussed elsewhere (Trevisan, 1987). TABLE3 Properties of the selected pesticides (Verschueren, 1983; Worthing and Walker, 1987). Ko~, Ko~ and Kb calculated (Fig. 2) 1

Atrazine Captan p,p' -DDT Diazinon Endosulfan HCB Lindane Malathion MCPA Molinate Vinclozolin

Activity

MW

H F C I I C I I H H F

215.7 300.6 354.5 304.3 406.9 284.8 290.8 330.3 200.6 187.3 286.1

m.p. (°C) 176 178 108 120 85 226 112 3 118 <0 108

Volatility (mPa) 0.04 <1.30 0.02 0.10 1200 1.45 5.6 5.3 0.20 746 < 10

Solubility (mg1-1)

Kow

Koc

Kb

30 3.3 0.003 40 0.325 0.006 7 145 825 880 1000

366 5.13,103 5.18,10 v 1.52,103 8.09,105 1.63 * 107 1.12,104 7.39,10 41.1 643 63.1

226 3.16,103 3.20,107 939 4.99,105 1.00,107 6.89,103 4.56,103 25.4 405 38.9

30.1 284 7.20* 105 101 2.10,104 2.69 * 105 551 388 4.70 48.7 6.77

~Abbreviations: Activity, I =insecticide; C =organo-chlorine insecticide; F = fungicide; H =herbicide. MW = molecular weight; m.p. = melting point; Solubility = solubility in water.

548 TABLE 4 Concentrations (ppb) in 'Padania' environment compartments estimated from ResPest-I, total amounts (millionsofmodelsyear- 1) estimatedby meansofthe compartmentvolumes,and amounts used in 'Padania' in 1983 (ISTAT, 1984)

Atrazine Captan DDT Diazinon Endosulfan HCB Lindane Malathion MCPA Molinate Vinclozolin

Air

Water

Soil (ppb )

Plants

ND1 0.075 0.276 0.075 0.001 0.001 0.005 0.076 0.124 0.134 ND

1.66 ND 26.97 8.07 1.11 0.26 2.18 0.31 13.00 83.00 ND

304.4 ND 24176.0 1624.6 544.0 250.0 6.3 6.9 ND 37.8 252.0 31.0 10.5 7.4 ND 0.7 14.0 14.0 18.0 ND 113.5 739.6

Men

Estimated Used (M moles )

ND ND 179.0 ND 1.1 296.0 15.8 ND ND ND ND

55.2 3152.4 245.1 64.7 6.8 37.0 19.5 2.4 159.0 1067.9 23.0

10.20 1.10 0.00 4.90 0.60 0.00 2.88 0.61 1.11 28.10 1.80

1ND= no available data. The following conditions were considered for the data extracted: (1) samples taken only in the 'Po-and-Adige' watershed area; (2) excluded were experimental data in which national regulations were infringed; (3) reference to agricultural products, soil, water, air and man. Average values of the extracted data for the different environmental compartments have been reckoned. Some drawbacks of the procedure have been discussed elsewhere (Del Re et al., 1987a,b). The results are shown in Table 4. RESULTS

Comparison between models The equilibrium concentration of the screened pesticides (Table 4) in the different models (Table 1 ) has been assessed by using 100 moles of product for km 2. The 'Kl~pffer', 'Terrestrial', and 'Rural' models required some adjustm e n t (see Fig. 3 caption). The distribution (Fig. 3a) varied, according to the description in McCall et al. (1983), but the concentration ratios were constant. This is shown in Fig. 3b, where the atrazine data have been assessed on the basis of a fugacity of 1/IPa for all models. Analogous results are obtained for other products; for the sake of brevity, only the data referring to D D T are reported (Fig. 3c). In all cases, the same expressions (Fig. 2) have been used to assess the partition coefficients Kow,

/(oc and Kb. It is obvious t h a t the choice of compartment volumes and their own numbers

549

0,3

[] AIR (ppb) [] WATER(ppm)

-

tO

0,2-

SEDIMENTS(ppm)

j~

0,1

~ AQ.BIOTA(ppm) PLANTS(ppm) [] MEN(ppm)

i~

O 0.0 1

2

3

4

5

6

7

Fig. 3A - Atrazine, 100 moles/kin2 0,05 w r0

0,04 0,03

C 0,02 0 C O O,Ol

o

0,00

J 1

.~

[] [] [] [] [] I~1

AIR (ppt) WATER(ppb) SOIL(ppb) SEDIMENTS(ppb) AQ.BIOTA(ppb) PLANTS(ppb)

_ 2

j 3

4

5

6

7

Fig. 3B - Atrazine, fugacity = 1 liPa 0,5 • 0,4O 0,3-

0,2" C 0

0

[] AIR (ppt) [] WATER(ppb) ~

j

SOIL(ppm)

SEDIMENTS(ppm) AQ.BIOTA(ppm) PLANTS(ppm) [] MEN (ppm)

0,1 0,0 1

2

3

4

5

6

Fig. 3C - DDT, fugacity = 1 llPa Fig. 3. C o n c e n t r a t i o n s in t h e c o m p a r t m e n t s o f 7 models (Table 1 ). 1 - - e c o s y s t e m , 2 = K l S p f f e r , 3 = O.E.C.D., 4 = rural, 5 = terrestrial, 6 = 6 - c o m p a r t m e n t s ' P a d a n i a ' [ P 6 ], 7 -- 8 - c o m p a r t m e n t s

'Padania' [P8 ]. Areas for the K15pffer,rural and terrestrial models have been reckoned as the area of an ecosystem model with the same soil volume. does not affect the predicted concentration. The result would be very different if the total amounts for each c o m p a r t m e n t {data not reported) were taken into account.

Pesticide amounts used in 'Padania' By reprocessing the ISTAT data (1984b), the pesticide amount annually

550 distributed in the 'Po-and-Adige' watershed (Table 4) could be assessed. Estimations of the total average amounts found in the 'Po-and-Adige' watershed are shown in the same table; they have been assessed by multiplying the average concentration in the compartments, whose data are available, by the volume of the compartments themselves. The average concentrations used in the calculations are shown in the following columns (Table 4). The amounts in the environment were an order of magnitude higher than the ones used annually. The only exceptions were: (1) DDT and HCB, for which no relation can be calculated, since their use has been banned; (2) captan, whose concentrations in the soil are very high, as reported in the literature, because it is used for agricultural treatments; therefore, the total quantity is liable to be overestimated; (3) molinate and MCPA, whose available data refer to rice-field waters, where the concentration is certainly higher than the average of surface waters.

Testing predictions of the 'Padania' model The testing predictions are shown in Table 5. Testing is possible for some compartments, but not for sediments and aquatic biota, owing to the total lack of empirical data.

Air The model predictions for air completely disagree with the experimental data. Many experimental data are obtained at the utmost limit of sensibility of sampling and measuring techniques, whereas other data refer to particular situations, for instance: vapours discharged by factories that produced DDT and diazinon, or sampling carried out immediately after treatment with MCPA and molinate.

Water The concentration in water is well in accordance with the model predictions in almost every case within a factor of 3. The only exceptions are DDT, endosulfan and HCB, whose predicted concentrations are 3 orders of magnitude lower than the experimental values. The predicted concentration range is not included in the sensitivity range of most analytical methods. Predicted concentrations are in the range of ppt in water, of ppm (DDT, HCB) or of tens of p p m (endosulfan) in suspended matter and in aquatic biota. Therefore, as water usually is not completely separated from suspended matter and aquatic biota during water-analysis procedure, it can be assumed that experimental concentrations in water are grossly overestimated. The model could be effectively tested by analyzing either water filtered, for example, at 0.2/~m, or deep-well, sterile water well filtered by underground clays.

551

~D

~9

v

¢$

°~

¢D

g

552

Soil Data and model prediction are in good qualitative accordance, even though there is a certain variability. For atrazine and vinclozolin, on the low side of the Kb distribution, experimental data are more than 10 times the predicted values; the opposite is observed in the case of diazinon and molinate.

Men and plants Both in men and in plants, experimental residue concentrations are lower than the predicted values, especially in the 'men' compartment; as for the plants, the predicted values are always higher when there is a discrepancy of one order of magnitude (diazinon, lindane, malathion). CONCLUSIONS

The adopted model has several limits, the most important of them, for the purpose of this work, being the absence of any consideration of degradation and transport phenomena. Within these limits, the model predictions are qualitatively in accordance with the experimental data as extracted from the ResPest-I database. Some negative features seem to be owing to relevant causes: (1) difficulties in water sampling and analysis; (2) interference of the organic and inorganic suspended matter with the organochlorine residue analysis in water; (3) the detoxification rates in living bodies may prove to be higher than the bioconcentration rate.

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