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A novel environmental indicator for monitoring of pesticides

A novel environmental indicator for monitoring of pesticides
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  Environ Monit Assess (2011) 177:151–163DOI 10.1007/s10661-010-1626-x A novel environmental indicator for monitoringof pesticides Federico Luchi · Valeria Vigni · Riccardo Basosi · Elena Busi Received: 3 September 2009 / Accepted: 9 July 2010 / Published online: 1 September 2010© Springer Science+Business Media B.V. 2010 Abstract  The identification of an indicator formonitoring pesticides is a very effective analyticalapproach because it allows one to schedule andsimplify the analytical routine. In this study, a newindicator has been designed, which has to be ableto define a scale of priorities in the pesticidesmonitoring. A starting equation was developedinvolving the escaping tendency of a given sub-stance from a phase (based on the Mackay modelI level). The reliability of the indicator was testedby application to a model system, consisting of adefined and limited area, choosing water as matrixover a period of 6 years. A group of marker com-pounds was also defined to implement the predic-tive efficacy of the indicator. The results obtainedby modeling were compared to those obtainedby experimentation of the same model system. F. Luchi · R. Basosi · E. Busi ( B )Department of Chemistry, University of Siena,Viale Aldo Moro n. 2, 53100 Siena, Italye-mail: busie@unisi.itV. VigniDepartment of Siena, ARPAT (Agenzia regionaleProtezione Ambientale), Strada di Ruffolo n. 1,Siena, Italy Present Address: F. LuchiLaboratorio di Sanità Pubblica Area VastaNord-Ovest Toscana, AUSL 2, Lucca, Italy The indicator was subsequently and appropriatelymodified creating a new equation, including akinetic factor, which considers the environmen-tal degradation processes. The effect of the rec-tified indicator was consistent with the sales datalist of compounds, when applied to the markers.The indicator developed in this study, tested asa model on specific area-phase-period (Provinceof Siena, water phase, 2000–2006), is applicable toany other area-phase-period, adjusting the parti-tion value of the Mackay model for the case understudy. Keywords  Environmental indicator · Pesticides monitoring · Model system · Multi-residual approach Introduction The identification of an indicator for monitoringenvironmental contaminants, residual from treat-ment in agriculture, is a very effective analyti-cal approach because it allows one to scheduleand simplify the analytical routine through costsavings and reduction of qualified personnel. Inparticular, it is very useful in the case of pesticidesbecause they are widely used and represent a wideranging and ubiquitous class of compounds. Infact, there are approximately 10,000 commercialpesticides in the world, deriving from at least 500,  152 Environ Monit Assess (2011) 177:151–163 chemically very different, active substances. It isestimated that each year, 250,000,000 tonnes of organic synthetic compounds are released into thebiosphere and 2,000,000 tons of those are pesti-cides, which are of the most toxic and persistentsubstances(Miller1994).Therefore,itisnecessaryfor the investigator, who verifies a particular envi-ronment, to make a choice on which substancesdeserve to be searched in order to reduce thenumber of analysis.In this study, 1 a new indicator was designed,which was able to define a scale of priorities inthe analysis of a given territory, of a given envi-ronmental matrix (water, soil, living organisms, orsediments) and of a specified period of time. Suchindicator would tell what analytes to look for asfirst in the environment. The indicator referred toa given substance has to be located in a prioritylist of compounds to be searched. What it wasrequired to the indicator was that its value inthe list must be consistent with the one actuallypresent in the analyzed matrix. In this way, boththe cost and time of a comprehensive screeningand the possibility of errors in random screeningis reduced.For calculating the indicator a number of issueswere taken into account:1. the local sales data of pesticides2. the capacity of each compound to be parti-tioned in the different matrices3. the different capacities of various substancesto be degraded in the environment(bioremediation)The reliability of the indicator was tested by appli-cation to a model system, consisting of a definedand limited area, choosing water as matrix in aperiod of 6 years. The obtained results were com-pared with those of experimental data performedon the same model system and the indicator sub-sequently and appropriately modified. 1 This study is a an extension of a PhD thesis of one of theauthors during his stay at the Department of Chemistry of the University of Siena until 2008. Materials and methods Indicator calculationThe indicator was set up by introducing an Excelspreadsheet data, crossing local sales data peryear (expressed as kilogram of active ingredi-ent extracted from the composition of a givenpreparation) to the equation that provided theenvironmental destiny. The local sales data wereconsidered as the maximum estimated amountthat could be poured into the territory.The feasibility of the calculation procedure wasexperimentally tested by applying it to a local-ized model where the area to be studied was theProvince of Siena; the period of time was theyears 2000–2006, choosing water as matrix. Thechoice of the area was made for practical reasonsof accessibility to the sales data and to the analysisof pesticides. Such criteria were indispensable forthe final evaluation of reliability of the method.The time period from 2000–2006 was chosen be-cause recently, since 2007, the area of analysis bypublic laboratories has changed in the macro-area(Siena-Arezzo) providing analytical data whichwere not anymore homogeneous with the previ-ous ones.The starting assumption of local sales data,as the maximum possible amount that could bepoured into the territory, was a good approxima-tion. In fact, the treatment with pesticides is reg-ulated by specific laws (REACH 2006) and thusmight be assumed to srcinate only from the localmarket because of licensing and transport costs.Moreover, the usage data in an inhomogeneousterritory were not easily available.The commercial name of each product wasprocessed in a database (,which gave back the name of the active compoundand its percentage in the srcinal formulation.Determination of partition volumesThe equation that provided the environmentaldestiny of a substance must consider firstly howthe substance was partitioned into different ma-trices. A good theoretical model of environmentaldistribution was srcinally introduced by Mackay,Level I, based on the concept of fugacity (Mackay  Environ Monit Assess (2011) 177:151–163 153 1982).Fugacitycouldberegardedasthe“escapingtendency” of a chemical substance from a phaseand was expressed in units of pressure. The firstlevel Mackay (1979) considered the equilibriumdistribution of a fixed amount of toxicant withouttransformation.Thisrequired  Z  valuesforthetox-icant in each compartment, which implied know-ing the physical chemicalparametersand partitioncharacteristics. An estimate was made of the totalamount of solute ( M   moles) likely to be presentin the entire environment at any given time. Thiscould be one years’ manufacture on a mol basis.If each compartment was assigned a subscript  i ( 1 ,  2 , 3 , etc.) then at equilibrium:  f  1  =  f  2  =  f  3  = ...  f  i  but M  =  C  i V  i  =  (  f  i V  i Z  i ) =  f  i  ( V  i Z  i ) Thus  f  i  = M   ( V  i Z  i ) And  M  i  =  f  i  V  i Z  i where  M  i  was the number of moles in each com-partment,   M  i  =  M  .This simple analysis was hypothetical since itignored inputs, outputs, and transformations, andit assumed that inter-compartment transfer wasfast. Therefore, it could not be used to predictconcentrations. It merely presented a picture of the ultimate distribution of a persistent substancein the environment in terms of both relative con-centrations and relative masses. This informationin itself was useful in throwing some light on thelikely compartments of concern.The first level Mackay considered a “worldunit” as a closed system at equilibrium and insteady-state conditions, where no degradation re-actions occurred. A “world unit” amounted to ≈ 1/500,000,000 of biosphere. In this system, whena substance was in a dynamic equilibrium betweenthe phases, its fugacity was the same in all phases(Mackay and Paterson 1981). Since the fugacitywas directly proportional to the concentration ineach phase, when M moles of a certain chemicalspecies were introduced in the system, the con-stants of proportionality  Z   can be calculated: C  =  Z f  Therefore,  Z  , expressed in Pa mol/m 3 , for eachphase, was the fugacity capacity and representedthe affinity of a molecule on the considered phaseand was calculated using the inverse relationship: Z   = C  /  f Z   calculation was different for each phase. Partic-ularly in the liquid phase, the fugacity was relatedto the concentration via the constant of Henry, P  = HC  , i.e., at the equilibrium  H   =  P  s /  s , where  s  was the solubility and  P   s vapor pressure of puresubstance, then: Z  w  = 1 / H   =  s / P  s This method, for calculating the capacity of asubstance to go in a given phase, was able topredict the destiny of environmental contami-nants knowing a minimum number of physical–chemicalpropertiesofthespeciestobetested,i.e.,the molecular weight, vapor pressure, solubilityin water, partition coefficient octanol/water  K  ow .The physical–chemical properties for each conta-minant were collected from various sources bothbibliographic (Tomlin 1994; Chiou et al. 1982) and on the web (CRPV 2009; Chemfinder 2000; CPCN 2009; ExToxNet 1998; FAO 2007; FITOGEST 2009; OSU 2009; PAN 2009; WIN-BDF (Borroni 2007)).Moreover, in this study, the “world unit” wasadapted for the current application because itdepends on the nature of the analyzed area. Forexample, the “world unit” proposed by MacKaywas a fraction of the biosphere of 1 km 2 in area,10 km 2 of atmosphere; the 30% of this area wascovered by soil, whose thickness was consideredto be 3 cm, and the remaining 70% was madeout of water, whose depth was set at 10 m, be-fore meeting a layer of sediment 3 cm (5 ppm of suspended solid particles and 0.5 ppm of livingorganic matter (biota)). As far as the territorialsituation of Siena, the values should be modifiedas follows: the overall area was 3,821 km 2 ; 234 km 2 of artificially shaped surface had to be removedbecause they are affected by construction andpaved roads. The active surface for the distribu-tionofcontaminantsinenvironmentalphaseswas,therefore, of 3,587 km 2 ; 3,557 km 2 of these werecovered with soil, both cultivated and woodland,  154 Environ Monit Assess (2011) 177:151–163 and 29 km 2 were surface water; therefore, onlyabout 1% of the considered surface was coveredby water (Fig. 1), unlike the 70% of Mackay“world unit” (Mackay 1982).Analytical experimentsThe analyticalquantificationof pesticidesin watermatrix were carried out at the Provincial Labo-ratory of the Department of Siena, for the years2001, 2005, and 2006 and at the Provincial Labora-tory of the Department of Florence in the periodof 2002–2004, using the same analytical method.This approach is called multi-residual method be-cause it allows the simultaneous quantification of several analytes in the same class of compoundswith savings of time and resources.Intheanalyticalprocedure,theaqueoussamplewas collected in dark glass bottles in order to pre-vent photo degradation of the active compoundsand initially processed to membrane filtration(PREPSEP prefilter, glass, binderless, 1.0  µ  m,47 mm) to remove any material in suspension,which could affect the next phase. The subsequentphase involved a solid phase extraction, whichconsisted of passing a known volume of sample(usually 1 l) through a material which was ableto absorb analytes in the aqueous matrix (solidphase extraction column: ISLANDS TRIAZINE Wetlands 0%Surface waters   1%Artificially modified terriories 6%Forest and semi natural areas 46%Farmlands 47% FarmlandsForest and semi natural areasWetlandsSurface watersArtificially modified terriories Fig. 1  Graphical representation of surface destinationsof the territories in the Province of Siena. The data werekindly provided by the Territorial Information System of the Province of Siena, Dr. Paolo Menicori by computerizedmapping of the province (SIGI - Province of Siena) 3 ml). Subsequent washing with water allowedthe removal of impurities, possibly present in theinitial sample, which would complicate the chro-matographic signals, or cover the peaks of theanalyzed compounds (Sicbaldi et al. 1997).The column was dried and eluted with theappropriate organic solvent, transferring the an-alytes in the test tube. The organic solutionwas evaporated under a stream of nitrogen andheating temperature below 40 ◦ C, dissolved in aknown volume of internal standard, and analyzedby gas chromatography. The instrument was aPerkin Elmer Turbochrom with a cross-linkedcolumn (5%,fenilmetilsiloxane, 25 m, 0.32 mm,film 0.17  µ  m); Gas purge: He (1 ml/min), Injec-tion temperature ECD; 250 ◦ C, Injection tempera-ture NPD; 250 ◦ C, Injection working mode ECD;Splitless 1 min, Injection working mode NPD;Splitless 1 min, Temperature NPD; 280 ◦ C, Tem-perature ECD; 350 ◦ C. The detector was athermionic detector, called nitrogen-phosphorusdetector or NPD, and electron capture detec-tor, ECD, respectively sensitive to the nitro-gen/phosphorus compounds and to compoundsthose are rich in electron-attractor atoms, such ashalogen and oxygen.In Table 1, the working conditions used in the Department of Siena are reported.The analytical results for each compound wereobtained through the comparison of recorded ar-eas, normalized to the internal standard, withthose of a solution at known concentration (con-sidering the concentration factor of the sam-ple), after confirmation by qualitative analysis ingas chromatograph detector with a mass spec-troscopy, which works under the same conditionsof gas chromatograph (see Table 1) and whosetechnical specifications were following: Instru-ment: THERMO Focus e DSQ II, cross-linkedcolumn (5%, fenil metil siloxane, 25 m, 0.32 mm, Table 1  Gas chromatography experimental conditionsTemperature ( ◦ C) Time (min) Ramp ( ◦ C/min)55 1 25150 5 5200 5 20270 10 25320 11 –  Environ Monit Assess (2011) 177:151–163 155 film 0.17  µ  m), Gas purge He (1 ml/min), Injec-tor Temperature 250 ◦ C, Injector working mode:Splitless (1 min), Source Temperature 200 ◦ C,Transfert-line Temperature 270 ◦ C, AcquisitionMode: Full Scan (50–450 u.m.a.)Different from the other gas chromatographydetectors, this system was able to give the massspectrum of each chromatographic peak; then,compared with those contained in computer li-brary, each signal was ascribed to the respectivechemical compound.During its development, the multi-residualmethod was statistically studied regarding its abil-ity to maintain the whole amount of the an-alytes, via recovery tests, whose values wereincluded in the range (70–130%) set by theUS Environmental Protection Agency (EPA) asan indication of accuracy (EPA 2005). Duringthis step was also estimated for each analytethe instrumental detection limit. An internal stan-dard, called “azoamide” (2,2,2-trifluoro- N  -{4-[( Z  )-phenyldiazenyl]phenyl}acetamide see Fig. 2) wassynthesized as follows: 4-phenylazoaniline andheptafluorobutyric anhydride were solved in ace-tonitrile and kept at room temperature for 1 h.All reagents (purchased from Sigma–Aldrich)and products were used without any further pu-rification. The molecule showed suitable molecu-lar properties to be with high intensity in all thedetectors used.The relative retention time for this internalstandard (R.R.T.) could be easily calculated bythe ratio between the retention time of each com-pound and the retention time of internal standard.The R.R.T. allowed monitoring changes inretention time due to random errors, such asthose related to manual injection, to tempera-ture fluctuations of the instrument during theprogram, etc. The internal standard was also Fig. 2  Molecular structure of the internal standard used for quantification: the ratio “area of eachpeak”/“area of internal standard peak” gives therelative Peak Area Ratio and minimized the de-tectors fluctuations and volume errors in differentinjections.Samples were collected from six rivers, 10 tor-rents, and three lakes. Specifications of collectionpoints were available upon request to the authors. Results and discussion The theoretical approach to calculate the newindicator was based on the formulation of a simpleequation. The application of this equation to thesales data list produced a list of priority for theanalysis on the chosen phase. Any difference of the two lists showed that the action of the indica-tor was not negligible. The obtained list of prior-ities could be subsequently improved by iterationof the starting equation.The starting equation was the following:  I  PHASE  =  Q SOLD ∗ ( % ) PHASE  (1)where  I  PHASE  was the indicator of a certain phase Q SOLD  was the amount in kg of active sub-stance obtained from the sales datacommercial products(%) PHASE  as the percentage of active substancepartitioned in the phase, calculatedwith Mackay model as described in“Materials and methods.” Comparing data obtained with the raw salesdata (Table 2) and the data obtained by previousequation (Table 3), it was apparent that only thetop three positions were the same in the two lists.The very large sold amounts for the top threeactive substances minimized indicator action.Sales data list should obviously be comparedwith the priority list of the following year.Comparing the two lists above, in order toascertain the impact of the proposed indicatoron the sales data list, it was apparent that therewere not differences in the first four positions, asconcerns mancozeb ,  glyphosate ,  fosetylaluminum ,
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