Institute of Occupational Medicine (IOM), Edinburgh, United Kingdom.
Health and Safety Executive (HSE), Buxton, United Kingdom.
Environ Res. 2024 Feb 1;242:117651. doi: 10.1016/j.envres.2023.117651. Epub 2023 Nov 22.
Long-term exposure to pesticides is often assessed using semi-quantitative models. To improve these models, a better understanding of how occupational factors determine exposure (e.g., as estimated by biomonitoring) would be valuable.
Urine samples were collected from pesticide applicators in Malaysia, Uganda, and the UK during mixing/application days (and also during non-application days in Uganda). Samples were collected pre- and post-activity on the same day and analysed for biomarkers of active ingredients (AIs), including synthetic pyrethroids (via the metabolite 3-phenoxybenzoic acid [3-PBA]) and glyphosate, as well as creatinine. We performed multilevel Tobit regression models for each study to assess the relationship between exposure modifying factors (e.g., mixing/application of AI, duration of activity, personal protective equipment [PPE]) and urinary biomarkers of exposure.
From the Malaysia, Uganda, and UK studies, 81, 84, and 106 study participants provided 162, 384 and 212 urine samples, respectively. Pyrethroid use on the sampling day was most common in Malaysia (n = 38; 47%), and glyphosate use was most prevalent in the UK (n = 93; 88%). Median pre- and post-activity 3-PBA concentrations were similar, with higher median concentrations post-compared to pre-activity for glyphosate samples in the UK (1.7 to 0.5 μg/L) and Uganda (7.6 to 0.8 μg/L) (glyphosate was not used in the Malaysia study). There was evidence from individual studies that higher urinary biomarker concentrations were associated with mixing/application of the AI on the day of urine sampling, longer duration of mixing/application, lower PPE protection, and less education/literacy, but no factor was consistently associated with exposure across biomarkers in the three studies.
Our results suggest a need for AI-specific interpretation of exposure modifying factors as the relevance of exposure routes, levels of detection, and farming systems/practices may be very context and AI-specific.
长期暴露于杀虫剂通常使用半定量模型进行评估。为了改进这些模型，更好地了解职业因素如何确定暴露情况（例如，通过生物监测进行评估）将非常有价值。
在马来西亚、乌干达和英国，在混合/应用日（并且在乌干达也在非应用日）期间，从农药施用者收集尿液样本。在同一天的活动前和活动后收集样本，并分析生物标志物，包括活性成分（AIs），包括合成拟除虫菊酯（通过代谢物 3-苯氧基苯甲酸[3-PBA]）和草甘膦，以及肌酸酐。我们对每个研究进行了多层次 Tobit 回归模型，以评估暴露修正因素（例如，AI 的混合/应用、活动持续时间、个人防护设备[PPE]）与尿液暴露生物标志物之间的关系。
在马来西亚、乌干达和英国的研究中，分别有 81、84 和 106 名研究参与者提供了 162、384 和 212 个尿液样本。在采样日使用拟除虫菊酯的情况在马来西亚最为常见（n=38；47%），而在英国（n=93；88%）使用草甘膦最为普遍。活动前后 3-PBA 的中位数浓度相似，与活动前相比，英国（1.7 至 0.5μg/L）和乌干达（7.6 至 0.8μg/L）的草甘膦样本的中位数浓度更高（草甘膦在马来西亚研究中未使用）。个别研究的结果表明，较高的尿液生物标志物浓度与当天尿液采样时的 AI 混合/应用、混合/应用持续时间较长、较低的 PPE 保护以及教育/读写水平较低有关，但没有一个因素在三个研究中的所有生物标志物中都与暴露一致相关。
我们的结果表明，需要对特定 AI 的暴露修正因素进行解释，因为暴露途径、检测水平和农业系统/实践的相关性可能因具体情况和 AI 而异。
