A continuing challenge for ergonomists has been to determine quantitatively the types of trunk motion and how much trunk motion contributes to the risk of occupationally-related low back disorder (LBD). It has been difficult to include this motion information in workplace assessments since the speed at which trunk motion becomes dangerous has not been determined. An in vivo study was performed to assess the contribution of three-dimensional dynamic trunk motions to the risk of LBD during occupational lifting in industry. Over 400 industrial lifting jobs were studied in 48 varied industries. The medical records in these industries were examined so that specific jobs historically categorized as either low, medium, or high risk for occupationally-related LBD could be identified. A tri-axial electrogoniometer was worn by workers and documented the three-dimensional angular position, velocity, and acceleration characteristics of the lumbar spine while workers worked at these low, medium, or high risk jobs. Workplace and individual characteristics were also documented for each of the repetitive lifting tasks. A multiple logistic regression model indicated that a combination of five trunk motion and workplace factors predicted well both medium risk and high risk occupational-related LBD. These factors included lifting frequency, load moment, trunk lateral velocity, trunk twisting velocity, and trunk sagittal angle. Increases in the magnitude of these factors significantly increased the risk of LBD. The analyses have enabled us to determine the LBD risk associated with combined changes in the magnitudes of the five factors. The results indicate that by suitably varying these five factors observed during the lift collectively, the odds of high risk group membership may decrease by over ten times. These results were related to the biomechanical, ergonomic, and epidemiologic literature. The five trunk motion and workplace factors could be used as quantitative, objective measures to redesign the workplace so that the risk of occupationally-related LBD is minimized.

Smoke from fire is a local, regional and often international issue that is growing in complexity as competition for airshed resources increases. BlueSky is a smoke modeling framework designed to help address this problem by enabling simulations of the cumulative smoke impacts from fires (prescribed, wildland, and agricultural) across a region. Versions of BlueSky have been implemented in prediction systems across the contiguous US, and land managers, air-quality regulators, incident command teams, and the general public can currently obtain BlueSky-based predictions of smoke impacts for their region. A highly modular framework, BlueSky links together a variety of state-of-the-art models of meteorology, fuels, consumption, emissions, and air quality, and offers multiple model choices at each modeling step. This modularity also allows direct comparison between similar component models. This paper presents the overall model framework Version 2.5 – the component models, how they are linked together, and the results from case studies of two wildfires. Predicted results are affected by the specific choice of modeling pathway. With the pathway chosen, the modeled output generally compares well with plume shape and extent as observed by satellites, but underpredicts surface concentrations as observed by ground monitors. Sensitivity studies show that knowledge of fire behavior can greatly improve the accuracy of these smoke impact calculations.

The veal industry experiences calf losses during the growing period, which represents a challenge to animal welfare and profitability. Health status at arrival may be an important predictor of calf mortality. The objectives of this prospective cohort study were to describe the health status of calves arriving at a veal farm and determine the risk factors associated with early and late mortality. Using a standardized health scoring system, calves were evaluated immediately at arrival to a commercial milk-fed veal facility in Ontario, Canada. Weight at arrival and supplier of the calf were recorded. The calves were followed until death or the end of their production cycle. Two Cox proportional hazard models were built to explore factors associated with early (≤21 d following arrival) and late mortality (>21 d following arrival). A total of 4,825 calves were evaluated from November 2015 to September 2016. The overall mortality risk was 7%, with 42% of the deaths occurring in the first 21 d after arrival. An abnormal navel, dehydration, housing location within the farm, arriving in the summer, and the presence of a sunken flank were associated with increased hazard of early mortality. Drover-derived calves and calves with a greater body weight at arrival had lower hazard of early mortality. Housing location within the farm, being derived from auction facilities, and an abnormal navel were associated with higher hazard of late mortality. These results demonstrate that risk factors for mortality can be identified at arrival, which represents a potential opportunity to selectively intervene on these calves to reduce mortality. However, methods of preventing the development of these conditions before arrival need to be explored and encouraged to improve the welfare of the calves entering the veal industry.

The findings suggest a significant mechanical spine loading cost is associated with low back pain resulting from trunk muscle coactivation. This loading is further exacerbated by the increases in body weight that often accompany low back pain. Patient weight control and proper workplace design can minimize the additional spine loading associated with low back pain.