Profiles and Predictor of Pesticide and Metal Mixtures in Urine Among Solar Greenhouse Workers

Le résumé fourni par la source

OBJECTIVE: The aim of this study was to evaluate the exposure profiles and predictors for solar greenhouse workers to chemical mixtures. METHODS: Two hundred eighty-one solar greenhouse workers in China were included in this study. Six pesticides and 14 metals in urine were determined using chromatography-mass spectrometry. Pearson correlation, k-means clustering, and principal component analysis were used. RESULTS: The Pearson correlation coefficient showed that the correlation between similar chemicals was stronger than that between different types of chemicals. The k-means clustering showed that the female workers and multiple greenhouse workers had significantly higher chemical concentrations. The principal component analysis results showed that six principal components explain over 50% of the data variance, each dominated by specific chemicals. CONCLUSION: This study provided important insights into the exposure characteristics and predictive factors of chemical mixtures among solar greenhouse workers.

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