首页 | 本学科首页   官方微博 | 高级检索  
     


Integration of biological monitoring, environmental monitoring and computational modelling into the interpretation of pesticide exposure data: introduction to a proposed approach
Authors:Colosio Claudio  Rubino Federico M  Alegakis Athanasios  Ariano Eugenio  Brambilla Gabri  Mandic-Rajcevic Stefan  Metruccio Francesca  Minoia Claudio  Moretto Angelo  Somaruga Chiara  Tsatsakis Aristidis  Turci Roberta  Vellere Francesca
Affiliation:Department of Occupational and Environmental Health, University of Milan and International Centre for Rural Health of University Hospital San Paolo, S. Paolo Hospital Unit, Via San Vigilio 43, 20142 Milan, Italy. claudio.colosio@unimi.it
Abstract:Open field, variability of climatic and working conditions, and the use of complex mixtures of pesticides makes biological and environmental monitoring in agriculture, and therefore risk assessment and management, very complicated. A need of pointing out alternative risk assessment approaches, not necessarily based on measures, but simple, user-friendly and reliable, feasible also in the less advanced situations and in particular in small size enterprises, arises. This aim can be reached through a combination of environmental monitoring, biological monitoring and computational modelling. We have used this combination of methods for the creation of "exposure and risk profiles" to be applied in specific exposure scenarios, and we have tested this approach on a sample of Italian rice and maize herbicide applicators. We have given specific "toxicity scores" to the different products used and we have identified, for each of the major working phases, that is mixing and loading, spraying, maintenance and cleaning of equipment, the main variables affecting exposure and inserted them into a simple algorithm, able to produce "exposure indices". Based on the combination of toxicity indices and exposure indices it is possible to obtain semiquantitative estimates of the risk levels experienced by the workers in the exposure scenarios considered. Results of operator exposure data collected under real-life conditions can be used to validate and refine the algorithms; moreover, the AOEL derived from pre-marketing studies can be combined to estimate tentative biological exposure limits for pesticides, useful to perform individual risk assessment based on technical surveys and on simple biological monitoring. A proof of principle example of this approach is the subject of this article.
Keywords:Pesticide exposure   Agriculture   Biological and environmental monitoring   Modelling
本文献已被 ScienceDirect PubMed 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号