A. Chiodini

A. Simonelli

C. Colosio

C. Minoia

C. Somaruga

N. Miraglia

N. Sannolo

N. Vitelli

P. Basilicata

R. Turci

S. Fustinoni

English

CHIODINI, ANNALISA NICOLETTA FEDERICA

SOMARUGA, CHIARA

VITELLI, NORA

AIR Universita degli studi di Milano

A combined modelling and monitoring approach to pesticide exposure assessmentC Colosio1,  P. Basilicata3, A Chiodini1, S. Fustinoni1, N. Miraglia3, R Turci, 2 A. Simonelli3,  C Somaruga1, N Vitelli3, C Minoia2, N Sannolo3.1 Department of Occupational Health of the University of Milan, S. Paolo Hospital Unit, Via Di Rudinì 8,20142 Milan (Italy); 2Laboratory for Environmental and Toxicological Testing – Salvatore Maugeri Foundation, via S. Maugeri 4, 27100 Pavia, Italy;  3 Experimental Medicine Department-Occupational Medicine, Industrial Hygiene and Toxicology Section, Second  University of Naples, Via L. De Crecchio 7, I-80138, Naples, Italy.Purpose. Exposure assessment is a key element in the overall risk assessment process. However,  reliable data can hardly be obtained in pesticide exposure assessment, since a number of factors can affect the repeatability of the measures and, therefore their representativity. This is the reason why algorithms and models are being developed. The aim of this study is to assess the variability of biological exposure measures in agricultural workers in an attempt to compare them with the estimates generated by algorithms. Methods. 34 subjects working in tomato cultivations in 20 farms located in the south of Italy, exposed to several different active ingredients; 48 vineyard workers mainly exposed to ethylenbisdithiocarbamates, and 12 subjects applying chlorpyrifos in Northern Italy, have been monitored. In order to collect comprehensive exposure data and information on pesticide application method, all participants completed a self-administered enrolment questionnaire. Recent exposure was assessed by measuring appropriate biomarkers in tomato workers, urinary ethylenethiorea in vineyard workers, and urinary 3,5,6-trichloro-2-pyridinol in pesticides applicators exposed to chlorpyrifos. The analytical procedures had been validated, including measurement uncertainty at high, medium, and low concentration levels. A repeated-measures design allowed to assess the reliability of the field data. Results. The collected data were found to vary widely over time. This finding suggests that biological monitoring could provide information on recent exposure only, which may be a limitation in the risk assessment process. Specific algorithms were therefore developed using selected variables affecting pesticide exposure, such as exposure time, use rate, concentration of active ingredient, formulation, number of applications/year, size of the treated area, application machinery, operator’s skills, use of personal protective equipment. The exposed subjects were divided in groups (no exposure, low exposure, medium exposure and high exposure). Seventy five percent of the predicted values were found to be consistent with the field data. Conclusions: Standardized exposure assessment algorithms are essential to provide sound risk assessment. Nevertheless, to generate models which can forecast exposure levels in typical exposure scenarios, field data need to be collected at frequent intervals. In this way a comprehensive database can be established. From such a dataset it is then possible to calibrate and validate robust models. In order to ensure the feasibility of these models, use of validated analytical procedures, including uncertainty measurement, and an effective repeated-measures design, are of utmost importance as well.Key words: pesticides; exposure estimate; modelling; biomonitoring.

A combined modelling and monitoring approach to pesticide exposure assessment

https://air.unimi.it/bitstream/2434/42192/1/ISBM2007%20Colosio%20et%20al.doc

A combined modelling and monitoring approach to pesticide exposure assessment

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AIR Universita degli studi di Milano