An educational intervention on the risk perception of pesticides exposure and organophosphate metabolites urinary concentrations in rural school children in Maule Region, Chile.

BACKGROUND: Organophosphate (OP) pesticides can be hazardous to human health if not applied with appropriate precautions. There is evidence in the Maule region of Chile that rural schoolchildren are exposed to OP pesticides. OBJECTIVE: To evaluate the effectiveness of an educational intervention on OP exposure and understanding of pesticides and their hazards (risk perception) in two school communities in the Maule Region of Chile during 2016. METHOD: We conducted a quasi-experimental study about the effects on OP pesticide exposure of a community outreach and education program (COEP) administered in four 2-h sessions that's included hands-on activities among 48 schoolchildren from two rural schools. The intervention was directed to groups of parents and school-children separately, and aimed to educate them about the risks of exposure to pesticides and their effects on health. We measured 3,5,6-trichloro-2-pyridinol (TCPy), 2-isopropyl-4-methyl-6-hydroxypyrimidine (IMPY), malathion dicarboxylic acid (MDA), p-nitrophenol (PNP), specific urinary metabolites of the OP pesticides chlorpyrifos, diazinon, malathion and parathion, respectively, as well as the non-specific diethylakylphosphates (DEAPs) and dimethylalkylphosphates (DMAPs) in 192 urine samples of schoolchildren collected before and after the intervention. The risk perception of school children and their parents was also assessed through a questionnaire before and after the intervention. Generalized Estimated Equations were used to account for each child's repeated measures during four sessions, two in September 2016 (pre-intervention) and two in November 2016 (post-intervention). RESULTS: The intervention level had significant effect on the risk perception of adults and children, which increased after the intervention. However, the intervention was not associated with reduced of urinary metabolites levels, with no significant differences between the pre and post measures. The detection frequencies were 1.1% (MDA), 71.4% (TCPy), 43.3% (IMPY), 98.96% (PNP), and 100% (DEAPs and DMAPs). Higher DEAPs urine concentrations were associated with eating more fruit at school (p = 0.03), a younger age (p = 0.03), and being male (p = 0.01). DMAPs showed no associations with potential predictor variables (e.g. OPs applied at home, fruit consumption at school, among others). Higher TCPy was associated with attending a school closer to farms (p = 0.04) and living in a home closer to farm fields (p = 0.01); higher PNP was marginally associated with children younger age (p = 0.035). CONCLUSION: Environmental exposure to OP pesticides was unchanged even after behavior changes. It is possible that a longer time period is needed to observe changes in both behavior and urinary metabolites. The levels of DEP and DMP metabolites found here are above the reference population of the US, and our findings indicate exposure to a wide variety of OP pesticides. Given that individual-level interventions were not associated with lower exposures, efforts to reduce exposure must occur upstream and require stricter regulation and control of pesticide use by government agencies.

A Model Based on Clusters of Similar Color and NIR to Estimate Oil Content of Single Olives.

Lipid extraction using the traditional, destructive Soxhlet method is not able to measure oil content (OC) on a single olive. As the color and near infrared spectrum are key parameters to build an oil estimation model (EM), this study grouped olives with similar color and NIR for building EM of oil content obtained by Soxhlet from a cluster of similar olives. The objective was to estimate OC of individual olives, based on clusters of similar color and NIR in two seasons. This study was performed with Arbequina olives in 2016 and 2017. The descriptor of the cluster consisted of the three color channels of c1c2c3 color model plus 11 reflectance points between 1710 and 1735 nm of each olive, normalized with the Z-score index. Clusters of similar color and NIR spectrum were formed with the k-means++ algorithm, leaving a sufficient number of olives to perform the Soxhlet analysis of OC, as reference value of EM. The training of EM was based on Support Vector Machine. The test was performed with Leave One-Out Cross Validation in different training-testing combinations. The best EM predicted the OC with 6 and 13% deviation with respect to the real value when one season was tested with itself and with another season, respectively. The use of clustering in EM is discussed.