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Table 8 shows the results of the DelAzulPestRisk model for Cypermethrin indescriptive statistics of the distribution of values obtained after the application ofMonte Carlo. Figure 3 shows the probability distribution (lognormal) with a meanvalue quite low. Although the maximum value is around 90, the figure shows thatthis value has a negligible probability level. The P 95 of the probability distributionhas a certainty level greater than 90%.The sensitivity analysis performed to find which variables are the ones that areaffecting the most the total variance displays that the substance concentrationrepresents 98.6 % of the variance. Given the importance of this variable (alsoreflected in the high value of the variability coefficient (2.40)), it is clear thatpotential dangerousness would merit increasing the sampling of water quality giventhe high dispersion of concentration data of this substance.Table 7. Results of application of the DelAzulPestRisk model for the AggravatingFactor (AF).Substance Per a Log BCF AF2,4 D 2.40 0.50 2.90α – HCH 3.48 2.40 5.88β - HCH 3.48 2.40 5.88 - HCH 3.48 2.40 5.88γ- HCH 3.48 2.40 5.88Acetochlor 2.78 1.67 4.45Aldrin 4.28 3.96 8.24Cypermethrin 3.26 2.99 6.24Chlorpyrifos 3.26 2.94 6.20Endosulfan 4.38 2.19 6.57EndosulfanSulphate4.41 2.08 6.49γ - Chlordane 4.94 4.12 9.06Glyphosate 1.79 0.50 2.29Heptachlor 4.47 3.28 7.75a Persistence value (measured as 5- halflife)Table 8. Probabilistic results after applying the DelAzulPestRisk model.Substance Mean SD Median Min. Max. P95 CV3.54 8.49 1.20 1.15 9.12 1.390 2.40CypermethrinE +00 E +00 E +00 E -01 E +01 E +01 E +00AcademyPublish.org - The Impact of Pesticides256
Figure 3. Probability distribution model and descriptive statistics of the applicationof the DelAzulPestRisk model for Cypermethrin.ANALYSIS AND DISCUSSIONLevitan (1995) raises two issues that still remain in effect when designing the bestindex for the ranking of the environmental hazards of pesticides: how to include andweigh a wide range of environmental parameters when creating an integrated indexand how to achieve a balance between the large amount of information demandedby the analysis of a complex system and the real possibilities of obtaining it. In thisregard, due to cost and time required to achieve this wealth of information, many ofthe methodologies developed are considered expedient or "snapshot" assessmentsthat, at least, allow providing fairly immediate preliminary answers, in many cases,within environmental management programs (Swanson et al., 1997; Finizio et al.,2001 Padovani et al. 2004; De Smet et al., 2005).Maud et al. (2001) considers the wide dispersion of results that can be obtained fromapplying different rates to these pesticides. While acknowledging that an importantelement to consider in estimating the danger of pesticides is the volume ofdischarged substance into the environment, the complexity of the system subtracts"realism" by failing to incorporate the values of the site-specific variables. To this, itmust also be added the unique assumptions often held in the development of eachmodel, for example, in the allocation of weights to the variables (Finizio et al.,2001), causing the hazard ratings to be very little comparable.An intensive property is one that depends on the nature of the substance and not thequantity, while the extensive property depends on the amount of substance (MacKayet al., 2001). Thus, in this study, persistence is clearly an intensive property andbioaccumulation potential semi intensive one (depends on the LogKow of thesubstance and the fat body contents of the organism). On the other hand, theconcentration of the substance in water is a clearly extensive property. Today thereAcademyPublish.org - The Impact of Pesticides257
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The Impactof PesticidesEdited by:Pr
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Other access free resources from Ac
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The Impact of PesticidesPrepared an
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Table of Contentsp.203-p.224 Behavi
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About authorspublished by Elsevier.
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About authorsof medical researchers
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About authorsdifferent spatial scal
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About authorsEducation:Bachelor’s
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About authorsSensors - A Nanotechno
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Section 1HUMAN EXPOSURE TO PESTICID
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ecently explored on preliminary stu
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individuals within 1 day (Jokanovic
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occupational environmental health r
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must be considered, since effective
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Jensen BH, Petersen A, and Christen
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Ragas AMJ, and Huijbregts MAJ (1998
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Weichenthal S, Moase C, and Chan P
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human poisoning, general acute symp
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Management of the cholinergic syndr
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1982; Lotti, 1992; Jokanović et al
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exposed populations, revealed high
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DiazepamBenzodiazepines are CNS dep
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Among the many classes of oximes in
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inhibited by dichlorvos (a dimethox
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Dawson, 1995). It appears that the
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Eddleston M, Eyer P, Worek F, Moham
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Jokanović M, Kosanović M (2010b).
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Ray DE (1998). Chronic effects of l
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Worek F, Eyer P, Kiderlen D, Thierm
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developing countries is due to suic
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count and morphology, as well as se
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PON1 modulation of genetic damageFe
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elationship between OP exposure and
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Blatter-Garin MC, James RW, Dussoix
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Hung RJ, Hall J, Brennan P, Boffett
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Rojas-García AE., Solís-Heredia M
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Occupational Exposure to Pesticides
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considered as conditions typical of
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observed adverse health outcome. Su
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within cells can oxidize biomolecul
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of the study. A face-to-face questi
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PesticidesFungicideInsecticide-Nema
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Table 4. Mixtures of pesticides use
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In pesticide applicators, Table 7 s
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DISCUSSIONThe agricultural workers
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changes in the antioxidant enzyme C
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Bajpayee, M.; Pandey, A. K.; Parmar
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Environmental health criteria 155.
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Palus, J.; Rydzynski, K.; Dziubalto
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Singh, N.P.; McCoy, M.T.; Tice, R.R
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INTRODUCTIONPopulations may be occu
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exposure scenarios on the basis of
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not present adverse health effects
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greenhouse workers exposed to malat
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Paraoxon is then either metabolized
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cyclohexene-1,2-dicarboximide, CAS
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metabolite of phthalates, such that
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scenarios were simulated, assuming
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Table 2. Biological reference value
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References N a urinarybiomarkeStudi
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Figure 2. Representation of a BRV d
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Figure 4. Conceptual representation
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Figure 6 Conceptual representation
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REFERENCESACGIH, 2011. Documentatio
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Bradway, D.E., Shafik, T.M., 1977.
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EPA, 1999b. Registration Eligibilit
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Hines, C.J., Deddens, J.A., Jaycox,
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McCollister, S.B., Kociba, R.J., Hu
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Verberk, M.M., Brouwer, D.H., Brouw
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important IGFBP for binding IGF-1 i
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secretion of IGF-1 (Tannheimer et a
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FIGURESFig. 1. An “hormetic” ef
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Landi F, Capoluongo E, Russo A, Ond
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Smith WJ, Underwood LE, Clemmons DR
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Section 2PESTICIDES IN THEENVIRONME
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vicinity of subsoil waters and ther
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Hieracium pilosella, southernwood -
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formed clusters (Figure 3). Ultrast
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Fig. 5. Fragment of hepatocyte of t
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Szarek, J, Skibniewska, K, Wojtacka
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negative effects (ZALUCKI et al., 2
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Figure 2. Appropriated sampling pro
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Figure 4. Illustration of the conse
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(HAILE et al., 1998). Earlier resul
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Table 1. Distribution of the applic
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Means followed by the same letter i
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(involving pests and natural enemie
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efore the pest emergence and their
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Table 3 (continuation)…AcademyPub
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Table 5. Effect of different fungic
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Table 6. Magament used including do
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adopted by Brazilian soybean grower
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Pickle, C.S.; Caviness, C.E. Yield
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This chapter reports on Dutch surfa
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several different occasions at any
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The greatest number of pesticides a
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Accumulated ExceedanceIt is useful
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For the years 2003-2009 the calcula
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Bonzini S, Verro R, Otto S, Lazzaro
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atios of the pesticide amounts in t
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Page 208 and 209: mefenacet and simetrin were observe
Page 210 and 211: Table 2. (Cont.)Pesticide concentra
Page 212 and 213: Table 2. (Cont.)Pesticide concentra
Page 214 and 215: Fig. 3. Typical weekly variation of
Page 216 and 217: (BAM) in water (Pukkila et al. 2009
Page 218 and 219: Table 5. Concentrations of pesticid
Page 220 and 221: Table 6. Maximum concentrations of
Page 222 and 223: Iwafune, T, Inao, K, Horio, T, Iwas
Page 224 and 225: Suzuki, M, Takemine, S, Yoshida, M,
Page 226 and 227: factor, etc. Levels of pesticides i
Page 228 and 229: factor of 10 above ambient sea wate
Page 230 and 231: weight sample. Minimum were shown f
Page 232 and 233: Figure 3. DDTs in mussel samples fo
Page 234 and 235: Figure 5. HCB in mussel samples for
Page 236 and 237: Figure 8. Methoxychlor in mussel sa
Page 238 and 239: ACKNOWLEDGEMENTAuthors thank UNEP/M
Page 240 and 241: Pesticide Risk Index of Del Azul Wa
Page 242 and 243: on that presented in Swanson et al.
Page 244 and 245: eference value for that substance.
Page 246 and 247: predict the aquatic toxicity of a s
Page 248 and 249: Calculation Model of Aggravating Fa
Page 250 and 251: To estimate the values of Human Hea
Page 252 and 253: water in comparison with the durati
Page 254 and 255: Cypermethrin (95 th percentile (P 9
Page 258 and 259: are many ranking systems of dangero
Page 260 and 261: REFERENCESAres J. (2004). “Estima
Page 262 and 263: Mackay D. and Paterson S. (1991).
Page 264 and 265: Long-term Monitoring of Pesticides
Page 266 and 267: Sampling sites of the survey in 200
Page 268 and 269: than those in Lake Biwa and Seta Ri
Page 270 and 271: Fig. 7 Detection of fungicides in R
Page 272 and 273: Fig. 11 Detection of herbicides in
Page 274 and 275: Monitoring of pesticides in Yanamun
Page 276 and 277: A measured volume (1000 mL) of the
Page 278 and 279: SimetrynePretilachlorConcentration
Page 280 and 281: Fig. 18 Maximum concentrations of t
Page 282 and 283: Ecological risk assessmentFig. 20 Y
Page 284 and 285: REFERENCESAgradi E, Baga R, Cillo F
Page 286 and 287: Time Trend Variation of Selected Pe
Page 288 and 289: Table 1. Physical and chemical prop
Page 290 and 291: marketed under variety of trade nam
Page 292 and 293: Figure 3. Long-term trends of globa
Page 294 and 295: laboratory animals fed or injected
Page 296 and 297: fed for a lifespan at 5-1,600 mg/kg
Page 298 and 299: Soil samplingSoil sampling was foll
Page 300 and 301: Figure 5. Map showing sampling area
Page 302 and 303: concentration (MAC) in surface soil
Page 304 and 305: Table 7. Concentrations of HCH isom
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Shanghai and Guanting Reservoir (Ch
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Contrary to the results of ΣDDT, t
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DDT metabolites, the ratio of (p,p
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2001). Therefore, the predominance
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Mean concentration (ng g -1 dw)3002
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CONCLUSIONSAn evaluation of selecte
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Hung, D.Q., Thiemann, W., (2002),
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Viet, P.H., Hoai, P.M., Minh, N.H.,
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found in tissues or fluids samples
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Study area and SamplingThis study w
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the liver, mainly to p,p'-DDE and p
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33,370 ha, whilst that of woody cro
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Increase in crops area (x1000)Ha)25
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Table 1. Blood concentrations of or
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Council Directive 90/533/EEC of 15
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Konstantinou, IK, Goutner, V, Alban
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Smith, AG (2004). “Toxicology of
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Pesticides are used in order to pro
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EXPERIMENTALMaterialsIsoproturon wa
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RESULTS AND DISCUSIONEquilibrium is
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As seen in Table 4, as the concentr
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Fig.4. Breakthrough curves of isopr
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Fig.6. Breakthrough curves of isopr
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Fig.8. Experimental and theoretical
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Section 3PESTICIDE ANALYSISAcademyP
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scandal’ are familiar to the gene
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ecoveries (>70%) for certain pH-dep
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Due the versatility of the QuEChERS
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of application, with some still sca
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The main supercritical solvent used
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extracting the pesticides from the
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would be appropriate for the routin
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chromatography with negative-ion el
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GPC was applied as a nondestructive
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Charlton A.J.A., Stuckey, V. (2009)
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Lehotay, S.J., Mastvoska, K. & Yun,
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Rodrigues, F. M., Mesquita, P. R. R
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Analytical methods to assess the im
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acetylcholine, a neurotransmitter t
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1.3 to 22.3% for all pesticides. Li
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Malathion, parathion, fenitrothion,
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Fuller, BH and Berger, GMB (1990).
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Table 1. Largest exporters of coffe
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Fig. 1: Structures of (a) Phosphate
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ELECTROCHEMICAL BIOSENSORBiosensors
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inorganic and organic species a lar
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surface area and be easily modified
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Acetylcholine + H 2 O AChE Choline
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Wang, 2010) and tetracyanoquinodime
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PDDA/AChE/PDDA/CNT/GCAChE-Au-PPy/GC
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ng/mL and good precision (RSD ) 5.3
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FUTURE PERSPECTIVESThe detection of
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Du, D; Wang, M; Cai, J; Qin, Y; Zha
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Martorell, D; Cespedes, F; Fabreaga
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The Pesticide Action Network (PAN).
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About EditorMilan JokanovićProfess
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THE IMPACT OF PESTICIDESPesticides
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