Dermal exposure assessment of glyphosate herbicide among pesticide handlers in selected agriculture sectors in Malaysia
Malaysian farmers are still relying on pesticides to govern agriculture activities. Occupational exposure to pesticides can adversely affect farmers’ health, especially those in developing countries where the methods for evaluating dermal exposure are not reasonably well standardized. Therefore, thi...
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Format: | Thesis |
Language: | English |
Published: |
2021
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Online Access: | http://psasir.upm.edu.my/id/eprint/103771/1/NURULAIN%20BINTI%20MUSTAFA%20UDIN%20-%20IR.pdf http://psasir.upm.edu.my/id/eprint/103771/ |
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Summary: | Malaysian farmers are still relying on pesticides to govern agriculture activities. Occupational exposure to pesticides can adversely affect farmers’ health, especially those in developing countries where the methods for evaluating dermal exposure are not reasonably well standardized. Therefore, this study aimed to assess dermal exposure and health symptoms among pesticide handlers in selected agriculture sectors in Malaysia (i.e., paddy, vegetable, cocoa and oil palm). This cross-sectional study was conducted in 2016 in four sampling locations, been divided into three sub-studies which are; 1) Dermal exposure assessment using DREAM, 2) Dermal exposure assessment using whole-body dosimetry (WBD) method and 3) Identification of health symptoms. In the first sub-study, socio-demographic characteristics, information on pesticide application and dermal exposure information were assessed. Then, total potential (tPDE) and actual dermal exposure (tADE) among pesticide handlers were determined using DREAM model, and factors affecting the exposure were analysed using logistic regression analysis. In the second sub-study, the concentration of uranine as pesticide surrogate and total PDE were determined using the WBD method. The correlations between the measurements with tPDE from DREAM (Sub-study 1) were then analyzed by estimating the Spearman correlation coefficient. In the third sub-study, health symptoms of skin problems and pesticide poisoning perceived by respondents after pesticide spraying were identified. Then, the association between DREAM data and perceived health symptoms was determined through logistic regression analysis. Based on DREAM, tPDE estimates between different agricultural sectors were significantly different (χ 2 = 118.093, p < 0.001). The prominent highest exposure was recorded among paddy field sprayers (151.39 ± 22.64 DU), and the lowest exposure was among respondents in oil palm plantation (47.67 ± 18.47 DU). The factors significantly related to high tPDE (≥100 Dermal Unit) were pesticide deposition, sprayer’s age, gloves usage and immediate clothes changing after pesticide spraying. The total amount of uranine on Tyvek.
sections of respondents in the paddy field was 0.281 mg/kg, followed by respondents in oil palm (0.043 mg/kg), vegetable (0.042 mg/kg) and cocoa farming (0.013 mg/kg). The total potential dermal exposure (PDE) was significantly highest among respondents in paddy field with a mean total PDE of 1.56E-03±1.73E-04 (p < 0.001). A significant positive correlation was found between logged total PDE estimates from DREAM and logged total PDE measured through WBD (rs =0.752, p=0.001). Most of the respondents in the paddy field reported having skin itchiness (53.3%) and itchy wheals (24.4%), while respondents in vegetable farm reported increased salivation (29.4%), diarrhoea (14.7%), urinary frequency (23.5%), cramps (26.5%), muscle weakness (29.4%) and restlessness (17.6%). Total PDE from DREAM was the only variable that significantly contributed to the logistic regression model associated with skin problems and pesticide poisoning symptoms. Our study supports that DREAM able to provide estimates of exposure levels among respondents, gives insight into the distribution and route of dermal exposure, and can be associated with health symptoms. Nevertheless, DREAM estimates on variability in the distribution of dermal exposure on body parts over the measurement data should be further verified for model improvement. (494 words). |
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