928

Environmental Toxicology and Chemistry, Vol. 22, No. 4, pp. 928–935, 2003q 2003 SETAC

Printed in the USA0730-7268/03 $12.00 1 .00

PESTICIDE RISK ASSESSMENT IN A LAGOON ECOSYSTEM.PART I: EXPOSURE ASSESSMENT

SARA VILLA, ANTONIO FINIZIO, and MARCO VIGHI*University of Milano Bicocca, Department of Environmental and Landscape Science, Piazza della Scienza, 1 Milano Italy

(Received 28 November 2001; Accepted 25 September 2002)

Abstract—This work is the first step of a research project on pesticide risk assessment for a coastal lagoon ecosystem (Orbetellolagoon, central Italy). Pesticide exposure was evaluated by means of predictive and experimental approaches. Predicted environmentalconcentrations (PECs) for water and sediments were estimated using fugacity-based multimedia models. Pesticide loads werequantified through a study of the use to which agricultural land was put in the study area. Multimedia fate models were used topredict the environmental concentration of 29 active ingredients (mainly insecticides and herbicides) selected among the mostwidely used within the catchment basin. Worst-case environmental scenarios were developed for the application of these models,in agreement with the precautionary principle. Experimental monitoring was performed on 10 selected chemicals. To assess thevalue and limitations of predictive and experimental approaches, calculated PECs and monitoring results were compared anddiscussed. Combined modeling and monitoring approaches have been found to be the best way to date to assess proper exposure.

Keywords—Pesticides Coastal lagoons Exposure assessment Monitoring Modeling

INTRODUCTION

Coastal lagoons are environments of great natural and eco-nomic value due to the peculiarity of the biological community,which supports fisheries and extensive aquaculture. Lagoonsare particularly vulnerable and endangered systems because ofthe possibility of high pollutant loads from human activitiesin a shallow water body with low dilution potential and becauseof the relative fragility of the trophic chain [1].

The major problem of coastal lagoons is eutrophication [2],but in many cases, potentially toxic chemicals also play arelevant role. A list of pesticides damaging to Mediterraneanestuarine and coastal environments was recently drawn up [3].The need for protecting transitional coastal ecosystems (la-goons, estuaries) was highlighted in the European FrameworkDirective for community actions in the field of water policy[4].

To evaluate the need for protective measures and to identifypotential causes of danger, a preliminary risk assessment mustbe carried out. A risk assessment procedure can be developedthrough three main steps: assessment of environmental ex-posure of potentially dangerous chemicals, assessment of toxiceffects on living organisms, and assessment of the likelihoodof unwanted effects on the basis of toxicity/exposure ratio(TERs) or of predicted environmental concentration/predictedno-effect concentration (PEC/PNEC).

Environmental exposure may be assessed by experimentalmonitoring or by theoretical predictions through suitable fatemodels. Both approaches have values and limitations. Exper-imental monitoring cannot be planned correctly withoutknowledge of the distribution and fate patterns in differentenvironmental compartments. Many highly toxic chemicals(e.g., some insecticides) could exert sublethal effects at verylow concentrations; consequently, negative findings in exper-

* To whom correspondence may be addressed(marco.vighi@unimib.it).

imental monitoring cannot be assumed to guarantee absenceof risk.

On the other hand, site-specific predictive models need in-put data that are often difficult to obtain with the necessarylevel of detail and precision. Assumptions and approximationsare therefore needed, which in turn need to be validated andcalibrated by means of experimental data. Thus, experimentaland theoretical approaches can be fruitfully used as comple-mentary tools in exposure assessment.

For the purposes of this article, pesticide exposure in theOrbetello lagoon was assessed by means of an integrated ap-proach using experimental monitoring and fugacity-based mul-timedia fate models [5,6]. The main objective of this approachwas to develop and validate a preliminary procedure capableof providing the basic information needed for a screening-level risk assessment applicable to a large number of chemicalsthat could endanger a natural ecosystem. Under real site-spe-cific conditions involving a relatively large area, it might bedifficult to obtain reliable data on environmental characteristicsand pesticide load. In many cases, therefore, approximated dataor default worst-case scenarios were applied. The scope of thiswork is to verify whether such a procedure can produce resultsof satisfactory reliability.

MATERIALS AND METHODS

Description of the area studied

The Orbetello lagoon (Fig. 1), located between the southerncoast of Tuscany and the Monte Argentario promontory, isseparated from the sea by two narrow sandy dunes. The lagoonstretches for 27 km2 and is partially divided into two basins(the western and the eastern lagoons) by the Orbetello isthmusand a dam, which connects Orbetello to the Monte Argentario.

Water exchange with the sea occurs through the S. Liberatachannel in the western lagoon and the Ansedonia channel inthe eastern lagoon. Pesticide inputs into the Orbetello lagoonmainly come from the Albegna River, which collects water

Pesticide risk in a lagoon ecosystem Environ. Toxicol. Chem. 22, 2003 929

Table 1. Surface area of major crops in the Albegna catchment area(Italy)

Crop Surface (ha)% Total basin

surface

Wheat, oatsVineyardSunflowerOlive grovesSugar beetTomatoesOnionMelonPear treeOther cropsTotal cultivated areaTotal basin surface

11.6182.2382.0511.867

666192

802711

2.81421.56473.600

15.832.82.510.30.1

,0.1,0.1

3.829

100

from an important agricultural area and is connected to thelagoon through the Fibia channel. The hydraulic regime is quitecomplex [7–9]. Natural circulation occurs during the cold sea-son and occasionally during the warm season. In spring andsummer, circulation is usually forced (Ansedonia and S. Lib-erata pumps and bulkhead at Fibia channel). The water, eitherin natural or in forced circulation, flows from the river to orfrom the lagoon according to the sea tide and wind force anddirection [8].

Albegna is a typical torrent-like river, with maximum flowduring autumn and spring (Qmax 5 275 m3/s in December) andlow water in the summer (Qmin 5 0.24 m3/s in September) [10].The catchment surface measures about 730 km2 and is mainlyhilly (0–400 m above sea level), although a small part is moun-tainous (Monte Amiata, Italy).

Model selection and description

The predictive approach was based on two different fu-gacity-based models. Multimedia models usually treat the en-vironment as a well-mixed set of connected and homogeneouscompartments into which a chemical is introduced under var-ious stable or dynamic conditions and to which a selection ofsimplifying assumptions is applied.

For most environmental parameters, the data available werenot detailed enough to describe environmental variability.Weighted averages were therefore used, assuming the param-eter to be homogeneous within the system. Moreover, when-ever data were not precise enough to define a single figure,worst-case and best-case scenarios were developed to definea probable range of variability of concentrations.

Pesticide loads into the lagoon system were estimated usingthe SoilFug model [11]. SoilFug was specifically developed topredict pesticide contamination in surface water from surface andsubsurface (tillage layer) runoff and was successfully validatedat field scale as well as at the scale of medium-size catchments[12–14]. A sensitivity analysis to assess the role of the maindriving force was performed by Barra and others [15]. The soft-ware SoilFug, Ver 1.2, was used, available on request at www.trentu.ca/academic/aminss/envimodel/SoilFug.htlm.

The distribution and fate of pesticides within the lagoonsystem were evaluated using a level I fugacity model [5]. Thelevel I model describes the equilibrium distribution of a fixedquantity of chemicals among the media, such as there beingno inputs or outputs, and no degradation process is assumed.The use of a more sophisticated model (level III) was precludedby the lack of reliable information on some input data, inparticular on persistence in sediment and water. For most pes-ticides, persistence data are traceable in soil. Complete infor-mation on degradation in all environmental matrices (in par-ticular salt water and sediments) is available for a relativelysmall number of chemicals among the selected pesticides, andthis hampers the possibility of a comparative screening. There-fore, a level I model was deemed to be more suitable, even ifapproximated, for the assessment of peak concentrations in thelagoon, corresponding to peak events in the river.

Land use and pesticide inventory

Some assumptions and approximations were needed be-cause updated information for the 1999 sampling year was notavailable at the time. Information on crop distribution in theAlbegna basin was obtained from official national statistics[16] (http://www.istat.it/English/index.htm), which was inte-

grated with data from local authorities (Provincia di Grosseto)and with personal information from local experts.

Major crops and their surface areas are reported in Table1. Reliable records on pesticide applications were not avail-able. Therefore, different approaches were used to create thepesticide inventory. Inquiries were instituted on selectedfarms, representative of local agriculture and covering about30% of the total cultivated area; a questionnaire was submittedinquiring about pesticides used, amounts applied, period ofapplication, etc.; and information on the pesticides sold wasobtained from the local agricultural cooperative. This infor-mation was useful on a qualitative rather than a quantitativebasis because farmers are able to buy chemicals outside thearea, and information on current agricultural practices wasobtained through personal contacts with local experts.

All this information was combined to produce a realisticpicture of the use made of 35 active ingredients. Of these 35,6 were discarded either because their fate patterns were notpredictable with fugacity-based models (polymeric or ioniz-able chemicals) or because of the lack of an existing physical-chemical database. Thus, a series of 29 chemicals was selectedfor a modeling approach.

The list of chemicals used, the amounts applied, the ap-plication times, and the physical-chemical properties areshown in Tables 2 and 3. Given the uncertainties of the esti-mating approach, two different use scenarios (a worst-case,indicating the potential maximum load, and a best case, whichis probably more realistic) were hypothesized. For some chem-icals, the information was sound enough and differences be-tween the two scenarios negligible.

Environmental scenarios for models

For the SoilFug method to work, it requires some soil char-acteristics (textural and structural properties, organic carboncontent, soil thickness) as well as water balance (timetable andintensity of rain events, quantity of runoff water).

In this case, the soil characteristics were selected to simulatean average of soil characteristics in the study area. The averagetemperature was 208C (the selected average refers to the pes-ticides application period, March–September), the organic car-bon fraction 0.015 g/g, and air and water fractions 0.2 m3/mand 0.3 m3/m, respectively. Air and water volume in soil areat field capacity. For the application of the SoilFug model, asoil depth ranging from 0.05 to 0.3 m is normally used. A soildepth of 0.1 m was applied in the Albegna basin on the basis

930 Environ. Toxicol. Chem. 22, 2003 S. Villa et al.

Table 2. Amounts of active ingredients (a.i.) applied and application times utilized in the area studied (I 5 insecticides, H 5 herbicides, F 5fungicides). For the active ingredients applied on more than one crop, the number of treatments and the respective application periods are dividedby a slash (/). For the molecules applied more than once on the same crop only (see details in the text), the application times are divided by acomma. The Roman numerals indicate the period of the month in which application occurred (I, days 1–10; II, days 11–20; III, days 21–30)

Listnumber Active chemical Crop

Applicationrate

(kg a.i./ha)Worse/best area

(hectare)Number oftreatments Treatment period

1 I Azinphos-methyl Sugar beet/peach tree 0.37/0.5 677.1–510.6 1/4 April III/April II, MayJun I, III

2 H Bromoxynil Wheat, oats 0.4 11,618–11,246.3 1 March I3 H Chloridazon Sugar beet 2.5 666.1–499.5 3 March I, II, III4 I Chlorpyrifos-methyl Vineyard 0.33 2,238–401.3 1 August III5 H Chlorthal-dimethyl Onion 15 80–51.4 1 March II6 F Cymoxanil Onion/vineyard 0.5 2,318–2,289.4 2 May II, June I7 F Dichlofluanid Vineyard/tomatoes 0.7 2,430–593.5 1/2 June I/July II, III8 I Dimethoate Olive groves 0.3 1,867 1 August II, September9 H Ethofumesate Sugar beet 0.5 666.1 3 March I, II, III

10 F Fenarimol Vineyard/melon 0.2 2,265–1,863.3 2/2 May III, July II/July I,11 I Fenitrothion Vineyard 0.35 2,238–1,836.6 1 May III, July II12 I Furathiocarb Onion 3 80–51.4 1 April II13 H Imazamethabenz-methyl Sunflower 0.12 2,051–389 1 June I14 F Ioxynil Wheat, oats 0.3 11,618 1 March I, II15 I Isofenphos Onion 2.4 80–51.4 1 April II16 H Linuron Sunflower 0.34 2,051–833.6 1 May I17 F Metalaxyl Onion/vineyard 1 2,318–1,888 2 May III, July II18 H Metamitron Sugar beet 2.8 666.1–499.6 3 March I, II, III19 H Metobromuron Sunflower 0.4 2,051–389 1 June I20 F Oxadixyl Onion/vineyard 0.2 2,318–2,289.4 2 May III, July II21 H Oxyfluorfen Sunflower 0.03 2,051–772.4 1 March III, April I22 F Penconazole Melon/vineyard 0.2 2,265 2/2 May III, July II/July II,23 H Pendimethalin Onion/sunflower 1.5 2,102.4–1,274 1 May I24 I Phoxim Onion 1 80–51.4 1 April II25 F Procymidone Vineyard 0.5 2,238–401.3 1 August II26 H Propachlor Onion 5.9 80–51.4 1 March II27 H Sethoxydim Sunflower 0.2 2,051–833.6 1 June I28 H Tralkoxydim Wheat, oats 1.5 11,618–8,331.8 1 March I, II29 H Trifluralin Tomatoes 0.9 192 1 May III

of the soil characteristics detected and the agricultural practices(tillage depth) carried out.

Daily rainfall records, collected from 1951 to 1998, wereused to develop a rain event (RE) timetable from the beginningof March to the end of December. To simplify the water balancescenario, a single RE was hypothesized for each 10-d period,lasting for a period of time corresponding to the average num-ber of rainy days in the period calculated from the historicalrecords. The rainfall figure for the RE corresponds with therainfall average for the same period. The amount of wateroutput by runoff was estimated according to previous fieldexperiences [15,17]. Water output was assumed to be as highas 35% of the total input from rain on a yearly basis, rangingmonthly from 60 to 5% in relation to climatic conditions(evapotranspiration, rain amount, etc.). The outflows representthe net input of water leaving the basin as far as the lagoon.

Drift losses for insecticides and fungicides were not takeninto account. According to Ramos and others [18], for thetypical scenarios of Mediterranean crops, such as vineyardsand olive groves, characterized by high slopes with morestreams than lakes, drift is not as relevant as runoff for pes-ticide transport.

Historical records of daily water flow measurements in theAlbegna River were available. In this case, too, the water flowscenario was developed on the basis of the average of historicaldata. Concentrations in runoff waters predicted by the SoilFugmodel were used to calculate river concentrations by means

of dilution factors, calculated as the ratio between the riverwater flow and the amount of runoff water from the crop fields.

Finally, to assess the flow into the lagoon, the particularcirculation of the system was taken into account. The Orbetellolagoon undergoes forced circulation, especially during thewarm season, to avoid dystrophic/anoxic crisis due to eutro-phication. Nevertheless, a precise record of water flow is notavailable. To get a picture of the flow variability, differentscenarios were created taking into account the different factorsof lagoon hydraulic management: forced flow, with outputfrom the Ansedonia channel, and natural flow.

Forced circulation is the flow condition producing the max-imum chemical input into the lagoon, whereas with naturalcirculation, the input is reduced. Therefore, the two flow sce-narios were adopted as worst case and best case, respectively.

In some periods, natural and forced circulation present thesame values. During spring and summer, in fact, the Albegnaflow is low and, consequently, the water input into the lagoonthrough the Fibia channel is very small, independent of thelagoon’s hydraulic circulation.

The peak river concentrations of each chemical were usedas input for a fugacity level I [5] lagoon model. For the levelI fugacity model, the environmental scenario simply requiresthe volumes of the environmental compartments. Given thestructure and the characteristics of the lagoon, a volume of32.4E6 m3 (1.2-m depth, 27-km2 surface area) and a densityof 1.033 g/cm3 were assumed for the water compartment. For

Pesticide risk in a lagoon ecosystem Environ. Toxicol. Chem. 22, 2003 931

Table 3. Main physical-chemical properties of selected pesticides. Data taken from Finizio [28]. Data for half-life in soil (t½) were selected fromthe range of figures reported in the literature by choosing values recognized as reliable in relation to the environmental characteristics (climatic,

agronomic, geo-pedologic) of the study area

Listnumber Molecule

Molecularweight

Solubility(mg/L)

Vapor pressure(Pa) Log KOW t½ (d)

123456789

Azinophos-methyla

Bromoxynila

ChloridazonChlorpyrifos-methyla

Chlorthal-dimethyla

CymoxanilDichlofluanidDimethoatea

Ethofumesatea

317.34276.9221.6322.5332198.2333.2229.28286.3

29130400

40.5

1,0001.3

25,00050

3.0E25

6.4E24

,1.0E25

5.6E23

2.1E24

8E25

2.1E25

33.3E24

6.5E24

2.692.601.124.304.286.7E21

3.77.8E21

2.7

13102112

1001.527

56101112131415161718

FenarimolFenitrothiona

FurathiocarbImazamethabenz-methylIoxynilIsofenphosa

LinuronMetalaxyla

Metamitrona

331.2272.25382.5274.3370.9345.4249.11279.34202.2

143011

1,3705023.875

8,4001,700

2.93E25

1.3E24

3.9E26

1.47E26

1.0E23

4.0E24

2.0E23

7.5E24

2.0E26

3.693.394.61.823.434.042.761.701.3

36047

251830607030

1920212223242526

Metobromurona

Oxadixyla

OxyfluorfenPenconazolePendimethalina

PhoximProcymidonea

Propachlor

259.1278.3361.7284.2281.3298.3284.1211.69

3303,400

0.170

2.75E21

1.54.50

613

4.0E24

3.3E26

2.7E24

2.1E24

4.0E23

2.1E23

1.87E22

3.07E22

2.466.5E21

4.473.504.83.383.142.36

90–120180

30197360

477

272829303132333435

SethoxydimTralkoxydimTrifluralina

Clopyralidb

Dichlofop-methyla,b

Fluazinfop-P-butylb

Fluroxypyrb

MCPAa,b

Tribenuron-methylb

327.5329.4335.5192341.2383.4255200.6395.4

4,7006.12.21E21

300,0000.82

911,605

28,000

2.1E25

4E27

2.9E23

1.33E23

4.7E24

3.3E25

3.78E29

2.0E24

5.2E28

1.382.15.021.81

4.584.51.742.69

24.4E21

44

603030152715

4

a Pesticides included in the list of pesticides potentially damaging to the Mediterranean region [3]; 4-chloro-o-tolyloxyacetic acid (MCPA).b Chemicals not suitable for model predictions.

the sediment, it was assumed that only the upper 5-cm layerwas involved in chemical partitioning; therefore, a 13.5E5-m3

volume was calculated, with a standard density of 1.5 g/cm3

and an experimentally measured value of 12% of organic car-bon (dry wt). For suspended solids, a 162-m3 volume (50-ppm), a standard density 1.5 g/cm3, and 24% organic carbon(dry wt) were assumed, following Mackay [5], who suggestsan organic carbon content in suspended solids as high as twicethe content in sediments.

Sampling

Samples were collected in four sampling stations in thelagoon and one at the mouth of the river Albegna from Feb-ruary to September 1999 (Fig. 1). Sampling frequency wasrelated to the most important pesticide application periods andto climatic conditions. In general, samples were taken afterthe most relevant rain events. A total of 60 water samples and48 sediment samples were collected. Data from preliminarymonitoring [19] showed relative spatial uniformity. Therefore,two sampling stations for each lagoon were considered suf-ficient to describe the spatial variability.

Chemical analysis

Ten pesticides (Table 4) were selected as tracers accordingto their analytical capabilities, loads, physical-chemical prop-

erties, and fate patterns. A greater number of insecticides,compared with herbicides and fungicides, was chosen, mainlybecause of their high risk potential for the aquatic ecosystem,considering also the possibility of additive affects due to thesame toxicological mode of action as acetyl cholinesteraseinhibitors [20].

Water and sediment samples were stored in glass bottles at2208C until analyzed. Eight grams of each sediment samplewere homogenized, added to Na2SO4 (Merck, Darmstadt, Ger-many) and extracted with a mixture of hexane-acetone inSoxhlet for 12 h. The extracts were purified by a Florisilt(0.150–0.250 mm; Merck) column (2 g) and eluted with fourdifferent mixtures of hexane-acetone of increasing polarity.

Four liters of water were extracted using a C18 (Alltech,Deerfield, IL, USA) and Carbograph cartridge (CB) (Alltech),eluted by hexane-ethyl acetate (C18) or by dichloromethane-methanol (CB) mixture of increasing polarity. Finally, the ex-tracts were quantified in gas chromatography-mass spectrom-etry selected-ion monitoring mode.

Detection limits (Table 4) ranged between 0.1 and 10 mg/L for water and 0.007 and 0.07 mg/g for sediment and werecalculated according to Keith [21]. The recovery efficiency ofthe two procedures ranged between 70 and 100% for all activeingredients, in agreement with literature data [3,22–25].

932 Environ. Toxicol. Chem. 22, 2003 S. Villa et al.

Fig. 1. Map of the Orbetello lagoon (central Italy) indicating the sampling stations for analytical measurements (I, II, III, IV, Albegna River).

RESULTS AND DISCUSSION

Modeling results

Predicted peak concentrations in the river (Fig. 2) cover awide magnitude spectrum (from less than 0.01 mg/L to morethan 100 mg/L). Among the factors affecting river peak con-centrations, log KOW seems to be more important than the totalamount applied in the basin (Fig. 2 and Table 2). Persistenceplays a minor role in peak concentrations because a rain eventclose to application time was adopted.

Predicted concentrations in the waters and sediment of thelagoon are shown in Figure 3. For many compounds, twovalues are reported, corresponding to a worst and best caserelated to applications (Table 2) and water regime. The worstcase corresponds to the maximum application rate (worst casefor application) and forced circulation flow, while the best casecorresponds to the lower application rate (best case for ap-plication) and natural flow.

The load from the Fibia channel was calculated on the basisof the load in the Albegna River as follows:

L 5 L K L 5 C QF A A A A

where LF is the load into the lagoon from the Fibia channelduring the period of a rain event (g/10 d), LA is the loadtransported by the Albegna River during the period of a rainevent (g/10 d), CA is the pesticide concentration in the river(mg/m3), QA is the water flow in the river (m3/s), and K is a

factor representing the relationship between river and channelwater flow derived from experimental data [7,8].

Differences between worst and best case are generally with-in one order of magnitude. For some compounds (cymoxanil,dimethoate, oxadixyl, penconazole, trifluralin), differences arenegligible due to the correspondence between the two loadestimates and the two flow scenarios. This similarity takesplace during spring or summer, when the river flow is verylow and consequently the water input from the Albegna Riverinto the lagoon through the Fibia channel is reduced, apartfrom the lagoon hydraulic regime.

The predicted concentrations of pesticide ranged withinfour orders of magnitude in sediment and seven in water. Withthe exception of only three very hydrophilic chemicals (cy-moxanil, dimethoate, oxadixyl: log KOW , 1), all compoundsshow greater affinity with sediment. The PECs in suspendedsolids are twice those in sediments due to higher organic car-bon content.

Experimental data

Predicted concentrations for most of the active ingredientswere below detection limits (Table 4) both in water and insediments of the lagoon. Therefore, many negative results wereto be expected from experimental monitoring. Detectable con-centrations were found only of metalaxyl (sediments and sus-pended solids), while traces were occasionally observed in

Pesticide risk in a lagoon ecosystem Environ. Toxicol. Chem. 22, 2003 933

Table 4. List of pesticides for experimental monitoring. Reported below are analytical detection limitsin water and sediments as well as predicted concentrations in the lagoon. Also reported in this table arethe water quality objectives (WQO) proposed by the European Commission [27] for some chemicals

only

Chemicals

Detection limits

Water(mg/L)

Sediment(mg/g dw)

Predicted concentrations

Water(mg/L)

Sediment(mg/g dw)

WQO(mg/L)

Azinphos-methylChlorpyrifos-methylChlorthal-dimethylDimethoateFenitrothionIsofenphosMetalaxylOxadixylPendimethalinProcymidone

2.2 3 1021

1.1 3 1021

1.1 3 1021

2.2 3 1021

1.1 3 1021

1.1 3 1021

1.1 3 1021

1.1 3 1021

2.2 3 1021

1.1 3 1021

1.4 3 1022

3 3 1023

3 3 1023

3 3 1023

3 3 1023

3 3 1023

3 3 1023

3 3 1023

3 3 1023

3 3 1023

2.11 3 1022

3.44 3 1025

1.05 3 1024

3.12 3 1021

8.65 3 1024

5.19 3 1024

1.658.45 3 1021

1.41 3 1024

1.31 3 1023

5.85 3 1024

8.44 3 1026

1.24 3 1024

1.45 2 1024

1.3 3 1024

2.04 3 1025

4.58 3 1023

2.13 3 1024

1.44 3 1025

3.22 3 1025

0.010.01

0.01

Fig. 2. Relationship between the maximum predicted river concen-tration and log Kow for each active chemical. Numbers identify com-pounds as listed in Table 2.

Fig. 3. Predicted environmental concentration (PEC) of pesticides inthe water and sediments of the lagoon. For some compounds, twovalues are reported, corresponding to the worst-case (l) and best-case (m) scenarios as reported in the text. Underlined chemicals arethose selected for experimental monitoring. The line represents iso-concentration in water and sediments. Numbers identify compoundsas listed in Table 2.

water for oxadixyl and dimethoate, although difficult to quan-tify because they were close to detection limits (0.1 and 0.2mg/L for oxadixyl and dimethoate, respectively) (Table 5). Thevery sporadic occurrence in water may justify the lack of de-tection in this compartment of metalaxyl, showing predictedconcentration close to oxadixyl and dimethoate.

Metalaxyl detections from May to July coincide with theapplication periods (one taking place in May and then possiblya second in July), as do the sporadic detections of oxadixyland dimethoate, even if these last are less significant.

Comparison between predicted and experimental data

The modeling approach needed approximations and defaultassumptions, especially for environmental scenarios and pes-ticide use. Consequently, two alternative scenarios (worst andbest) were developed. The differences between worst- andbest-case scenarios were, at any rate, within one order of mag-nitude. Taking into account the large variability of concentra-tions among different active ingredients, this level of approx-imation could be considered as acceptable, at least for clas-sifying chemicals into different exposure levels.

In spite of its limits, the level I model for the lagoon en-vironment was selected to highlight those chemicals with high-er potential hazards. A level III would generate much morerealistic PECs, but the lack of degradation constants, required

as input data, would compromise the completeness of PECassessments and consequently the hazard list.

Although not sufficient for a proper calibration, the ana-lytical data can be considered as being in reasonable agreementwith the predicted results. The scarce positive evidence, in-cluding chemicals detected as traces, is in agreement withpredictions. For all nondetected chemicals, PECs were belowdetection limits both in water and in sediments.

A comparable agreement between observed and predictedconcentrations was not observed for the river. The PECs seemgenerally to overestimate the measured concentration. This canbe explained by the higher time variability of river concen-trations, strongly dependent on water flow and rain events,while lagoon concentrations are more stable over a period. Itmay be concluded that the sampling scheme was not detailedenough for a good characterization of river concentrations, butit was detailed enough for the lagoon.

CONCLUSIONS

The exposure assessment is a fundamental step of a riskassessment procedure, and in a recent document [26], the value

934 Environ. Toxicol. Chem. 22, 2003 S. Villa et al.

Table 5. Analytical results for the river and the lagoon systems. Traces, for sediments and suspendedsolids, refer to a concentration in the range of detection limit and quantification limit. Traces for oxadixyland dimethoate in water are concentrations that lie in a range of 0.1 to 0.05 mg/L for oxadixyl and of 2

to 1 mg/L for dimethoate (ND, not detected)

Sampling point

Metalaxyl(mg/kg)

SedimentSuspended

solid

Oxadixyl(mg/L)Water

Dimethoate(mg/L)Water

May IIIIIIIIV

0.0160.0160.0190.017

NDNDNDND

NDNDNDND

NDNDNDND

May IIIIIIIIVAlbegna River, Italy

0.0250.0200.0130.023ND

TraceTraceTraceTraceTrace

NDNDNDNDND

NDNDNDNDND

JuneIIIIIIVAlbegna River

0.062NDNDND

TraceTraceTraceTrace

NDNDNDND

NDNDNDND

JulyIIIIII

0.0620.0520.040

NDNDND

NDNDTrace

NDNDND

IV ND ND ND Trace

and limitations of predictive and experimental approacheswere highlighted. A combined strategy, uniting models andexperimental monitoring, seems to be a good approach to over-coming the limits and uncertainties of both methodologies.

The model approach is an effective instrument for pre-dicting the environmental concentration at the basin scale, atleast in comparative terms. It makes it possible to producePEC time trends on a large number of chemicals, even at levelsfar below the analytical detection limit. It should be taken intoaccount that water quality objectives for pesticides are oftenfar below analytical detection limits [27] (Table 4) and thatthe lack of any positive experimental finding does not nec-essarily mean absence of risk. In particular, the possibility ofpredicting low concentrations could be relevant when assess-ing the potential risk of chemicals with the same mode ofaction, such as carbamates and phosphorganic acetyl colines-terase inhibitors, capable of exerting additive effects in a mix-ture [20]. Moreover, the time variability of concentrations, inparticular in rivers, makes reliable and cost-effective moni-toring difficult.

On the other hand, validation of assessed PEC with mon-itoring results is necessary to check the reliability of predictedvalues. Collecting data for developing environmental scenariosis the most difficult problem for environmental fate modeling.The difficulties in collecting information on input data led tothe need to use default figures or theoretical assumptions fordeveloping realistic (but not real) environmental scenarios.The experimental validation of a number of chemicals selectedas tracers may confirm the reliability of modeling scenarios.

Acknowledgement—This project was supported by a fund made avail-able by the Ministero dell’Universita e della Ricerca Scientifica eTecnologica.

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