INTRODUCTION: Aircraft maintenance workers may be exposed to organophosphates in hydraulic fluid and engine oil. Previous research has indicated that inhalation may not be the primary exposure route. This study sought to measure dermal contact and inhalation in conjunction with cholinesterase inhibition and determine if Air Force Specialty Code serves as an exposure predictor.METHODS: Aircraft maintenance workers were sampled for changes in acetylcholinesterase and butyrylcholinesterase. Dermal contact was measured using wrist-worn silicone passive dosimeters and inhalation exposure was measured using thermal desorption tube air sampling.RESULTS: Overall prevalence of any cholinesterase inhibition in the study population was 25.33%. Prevalence of inhibition of acetylcholinesterase and butyrylcholinesterase was 18.67% and 6.67%, respectively. The mean tributyl phosphate result was 1.71 ng of tributyl phosphate per gram of wristband (ng g1) [95% confidence interval (CI): 5.63, 9.05]. Triphenyl phosphate was more prevalent, with only one sample below the limit of detection (mean 1386.26 ng g1; 95% CI: 7297.78, 10,070.31), and tricresyl phosphate was found in every sample (mean 4311.65 ng g1; 95% CI: 8890.24, 17,512.31). No organophosphates were detected via air sampling.DISCUSSION: Workers experienced organophosphate exposure and cholinesterase inhibition, but the study was not large enough to establish a statistically significant association between exposure and disease. Exposure to organophosphate esters is more likely to occur through contact and absorption of chemicals through the skin than through inhalation of oil mists. Air Force Specialty Code does not appear to be a good predictor of exposure to organophosphates. Future studies should consider using a larger sample size.Hardos JE, Rubenstein M, Pfahler S, Sleight T. Cholinesterase inhibition and exposure to organophosphate esters in aircraft maintenance workers. Aerosp Med Hum Perform. 2020; 91(9):710714.

After hazardous material incidents, it is important to perform emergency decontamination procedures to remove contamination from the body. As these emergency decontamination procedures are developed, it is important to understand the efficacy of a given protocol. This study discusses a method that was developed to evaluate the efficacy of decontamination procedures by using an ultraviolet fluorescent aerosol and an image analysis protocol. This method involves imaging a mannequin while both unclothed and clothed prior to exposure to the fluorescent aerosol. After exposure, it was imaged again, disrobed, and decontaminated following an unconscious patient wet decontamination method. This work describes in detail the materials and methods used to develop the final methodology. Two clothing types (black cotton and Tyvek) were used to simulate civilian and first responder casualties. Image analysis was used to measure the extent of contamination on the mannequin at each stage of the procedure. These measurements were then compared to determine decontamination efficacy for each step (disrobing, wet decontamination, and total removal). The exposure protocol was shown to provide repeatable deposition of aerosol onto the mannequin. Decontamination was also shown to be repeatable, with no trends toward efficacy changing over time.

Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative variables that are of predictive interest in gasification systems are gradient boosted machines and artificial neural networks. In this article, the reviewed literature used either gradient boosted machines or artificial neural networks to successfully model gasification systems. The review of such literature allows for a comparison in machine learning model architecture and resultant accuracy as well as an insight into what parameters are being used to inform the models and to make predictions.

Microwave-driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A two-dimensional microwave-driven plasma gasification reactor was developed in ANSYS (Ansys, Canonsburg, PA, USA) Fluent (a CFD tool), to create 644 (geometry and temperature) datasets for training six machine-learning (ML) models. When fed with just geometry datasets, these ML models were able to predict the proportion of the reactor area with temperature above 2000 K. This temperature level is considered a benchmark to prevent formation of undesirable byproducts. The ML model that achieved highest prediction accuracy was the feed forward neural network; the mean absolute error was 0.011. This novel machine-learning model can enable future optimization of experimental microwave plasma gasification systems for application in waste-to-energy.