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A pesticide exposure algorithm was developed to calculate pesticide exposure intensity scores based on responses to questions about pesticide handling procedures and application methods in a self-administered questionnaire. The validity of the algorithm was evaluated through comparison of the algorithm scores with biological monitoring data from a study of 126 pesticide applicators who applied the herbicides MCPA or 2,4-D. The variability in the algorithm scores calculated for these applicators was due primarily to differences in their use of personal protective equipment (PPE). Rubber gloves were worn by 75% of applicators when mixing and 22% when applying pesticides, rubber boots were worn by 33% when mixing and 23% when applying, and goggles were worn by 33% and 17% of applicators when mixing and when applying, respectively. Only 2% of applicators wore all three types of PPE when both mixing and applying, and 15% wore none of these three types of PPE when either mixing or applying. Substantial variability was also observed in the concentrations of pesticides detected in the post application urine samples. The concentration of MCPA detected in urine samples collected on the second day after the application ranged from less than < 1.0 to 610 microg/L among 84 of the applicators who applied MCPA. The concentrations of 2,4-D detected in the urine samples ranged from less than < 1.0 to 514 microg/L among 41 of the applicators who applied 2,4-D. When categorized into three groups based on the algorithm scores, the geometric mean in the highest exposure group was 20 microg/L compared with 5 microg/L in the lowest exposure group for the MCPA applicators, and 29 microg/L in highest exposure group compared with 2 microg/L in the low exposure group for the 2,4-D applicators. A regression analysis detected statistically significant trends in the geometric mean of the urine concentrations across the exposure categories for both the 2,4-D and the MCPA applicators. The algorithm scores, based primarily on the use of PPE, appear to provide a reasonably valid measure of exposure intensity for these applicators, however, further studies are needed to generalize these results to other types of pesticides and application methods.

This study presents advancements in the processing of satellite remote sensing data, focusing mainly on aerosol optical depth (AOD) retrievals from the Geostationary Environment Monitoring Spectrometer (GEMS). The transformation of Level-2 (L2) data, which includes atmospheric-state retrievals, into higher-quality Level-3 (L3) data is crucial in remote sensing. Our contributions lie in two novel improvements to the processing algorithm. First, we improve the inverse-distance-weighting algorithm by incorporating quality flag information into the weight calculation. By assigning weights that are inversely proportional to the number of unreliable grids, the method can provide more accurate L3 products. We validate this approach through simulation studies and apply it to GEMS AOD data across various regions and wavelengths. The use of quality flags in the algorithm can provide a more accurate analysis of remote sensing. Second, we employ a spatiotemporal merging method to address both spatial and temporal variability in AOD data, a departure from previous approaches that solely focused on spatial variability. Our method considers temporal variations spanning previous time intervals. Furthermore, the computed mean fields show similar spatiotemporal patterns to previous studies, confirming their ability to capture real-world phenomena. Lastly, utilizing this procedure, we compute the mean field estimates for GEMS AOD data, which can provide a deeper understanding of the impact of aerosols on climate change and public health. [ABSTRACT FROM AUTHOR]