Jiang Weiying, Hernandez Bernie, Richmond Donald, Yanga Nino
California Department of Pesticide Regulation, Sacramento, California 95812, USA.
J Expo Sci Environ Epidemiol. 2017 Jul;27(4):391-397. doi: 10.1038/jes.2016.36. Epub 2016 Jul 20.
Strawberry harvesters hand-pick fruit that may result in pesticide exposure from hand foliar contact. This paper included a review of publications on harvester pesticide exposure, an observation of their work activities, and development of an alternative model for pesticide exposure prediction. Previous studies monitored the dermal pesticide exposure of strawberry harvesters and found most of the exposure (>70%) was on the hands. Exposure rates (ERs) were calculated as pesticide amount on the skin per hour worked, assuming foliar contact is proportional to daily work hours. Transfer factors (TFs), used for predicting exposure, were calculated by dividing the ER by the amount of dislodgeable foliar pesticide residue. However, the ERs for harvesters working in the same field at the same time varied by as much as 10-fold, and TFs calculated from different studies varied by up to 100-fold. We tested the assumption of foliar contact time being proportional to daily work hours. We observed full work-day activities of 32 strawberry harvesters. We found that their foliar contact time per work minute differed by up to 46%. We suggested using the amount of strawberries picked to predict harvester foliar contact. For all observed harvesters, their foliar contact time per kg of strawberries picked was 35±5 s. This value was similar among harvesters with varying years of experience, of different gender, and using gloves or not. We proposed a predictive model using the amount of strawberries picked to predict harvester pesticide exposure. The exposure predicted by the model is close to the exposure measured in previous monitoring studies (R: 0.84). The model slope is 0.33±0.03 × 10 cm/kg. Model prediction accuracy was confirmed by monitoring captan exposure to harvesters in two fields. The model may be used as a quick screening method to estimate pesticide exposure before conducting complex human monitoring research.
草莓采摘工人工采摘果实，这可能会因手部与叶面接触而导致农药暴露。本文包括对有关采摘工人农药暴露的出版物的综述、对他们工作活动的观察以及开发一种用于农药暴露预测的替代模型。先前的研究监测了草莓采摘工人的皮肤农药暴露情况，发现大部分暴露（>70%）发生在手部。暴露率（ERs）的计算方法是每工作小时皮肤表面的农药量，假设叶面接触与每日工作小时数成正比。用于预测暴露的转移因子（TFs）通过将暴露率除以可去除的叶面农药残留量来计算。然而，同一时间在同一田地工作的采摘工人的暴露率相差高达10倍，不同研究计算出的转移因子相差高达100倍。我们测试了叶面接触时间与每日工作小时数成正比的假设。我们观察了32名草莓采摘工人的全天工作活动。我们发现他们每工作分钟的叶面接触时间相差高达46%。我们建议用采摘的草莓量来预测采摘工人的叶面接触情况。对于所有观察到的采摘工人，他们每采摘1千克草莓的叶面接触时间为35±5秒。在不同工作年限、不同性别以及是否戴手套的采摘工人中，这个值相似。我们提出了一个使用采摘的草莓量来预测采摘工人农药暴露的预测模型。该模型预测的暴露情况与先前监测研究中测量的暴露情况接近（R：0.84）。模型斜率为0.33±0.03×10厘米/千克。通过监测两个田地中采摘工人的克菌丹暴露情况，证实了模型预测的准确性。该模型可作为一种快速筛选方法，在进行复杂的人体监测研究之前估计农药暴露情况。
