Exposure Assessment to Radiofrequency Electromagnetic Fields in Occupational Military Scenarios: A Review

(1) Background: Radiofrequency radiations are used in most devices in current use and, consequently, the assessment of the human exposure to the radiofrequency radiations has become an issue of strong interest. Even if in the military field there is wide use of radiofrequency devices, a clear picture on the exposure assessment to the electromagnetic field of the human beings in the military scenario is still missing. (2) Methods: a review of the scientific literature regarding the assessment of the exposure of the military personnel to the RF specific to the military environment, was performed. (3) Results: the review has been performed grouping the scientific literature by the typology of military devices to which the military personnel can be exposed to. The military devices have been classified in four main classes, according to their intended use: communication devices, localization/surveillance devices, jammers and EM directed-energy weapons. (4) Discussion and Conclusions: The review showed that in the exposure conditions here evaluated, there were only occasional situations of overexposure, whereas in the majority of the conditions the exposure was below the worker exposure limits. Nevertheless, the limited number of studies and the lack of exposure assessment studies for some devices prevent us to draw definitive conclusions and encourage further studies on military exposure assessment.

Factors Associated with Levels of Organophosphate Pesticides in Household Dust in Agricultural Communities

Organophosphate (OP) pesticides are associated with numerous adverse health outcomes. Pesticide use data are available for California from the Pesticide Use Report (PUR), but household- and individual-level exposure factors have not been fully characterized to support its refinement as an exposure assessment tool. Unique exposure pathways, such as proximity to agricultural operations and direct occupational contact, further complicate pesticide exposure assessment among agricultural communities. We sought to identify influencing factors of pesticide exposure to support future exposure assessment and epidemiological studies. Household dust samples were collected from 28 homes in four California agricultural communities during January and June 2019 and were analyzed for the presence of OPs. Factors influencing household OPs were identified by a data-driven model via best subsets regression. Key factors that impacted dust OP levels included household cooling strategies, secondary occupational exposure to pesticides, and geographic location by community. Although PUR data demonstrate seasonal trends in pesticide application, this study did not identify season as an important factor, suggesting OP persistence in the home. These results will help refine pesticide exposure assessment for future studies and highlight important gaps in the literature, such as our understanding of pesticide degradation in an indoor environment.

A High Resolution Spatiotemporal Fine Particulate Matter Exposure Assessment Model for the Contiguous United States

Currently available nationwide prediction models for fine particulate matter (PM2.5) lack prediction confidence intervals and usually do not describe cross validated model performance at different spatiotemporal resolutions and extents. We used 41 different spatiotemporal predictors, including data on land use, meteorology, aerosol optical density, emissions, wildfires, population, traffic, and spatiotemporal indicators to train a machine learning model to predict daily averages of PM2.5 concentrations at 0.75 sq km resolution across the contiguous United States from 2000 through 2020. We utilized a generalized random forest model that allowed us to generate asymptotically-valid prediction confidence intervals and took advantage of its usefulness as an ensemble learner to quickly and cheaply characterize leave-one-location-out CV model performance for different temporal resolutions and geographic regions. Using a variable importance metric, we selected 8 predictors that were able to accurately predict daily PM2.5, with an overall leave-one-location-out cross validated median absolute error of 1.20 ug/m3, an R2 of 0.84, and confidence interval coverage fraction of 95%. When considering aggregated temporal windows, the model achieved leave-one-location-out cross validated median absolute errors of 0.99, 0.76, 0.63, and 0.60 ug/m3 for weekly, monthly, annual, and all-time exposure assessments, respectively. We further describe the model’s cross validated performance at different geographic regions in the United States, finding that it performs worse in the Western half of the country where there are less monitors. The code and data used to create this model are publicly available and we have developed software packages to be used for exposure assessment. This accurate exposure assessment model will be useful for epidemiologists seeking to study the health effects of PM across the continental United States, while possibly considering exposure estimation accuracy and uncertainty specific to the the spatiotemporal resolution and extent of their study design and population.