This study investigated the effects of faceseal leakage, breathing flow, and combustion material on the overall (non-size-selective) penetration of combustion particles into P-100 half and full facepiece elastomeric respirators used by firefighters. Respirators were tested on a breathing manikin exposed to aerosols produced by combustion of three materials (wood, paper, and plastic) in a room-size exposure chamber. Testing was performed using a single constant flow (inspiratory flow rate = 30 L/min) and three cyclic flows (mean inspiratory flow rates = 30, 85, and 135 L/min). Four sealing conditions (unsealed, nose-only sealed, nose and chin sealed, and fully sealed) were examined to evaluate the respirator faceseal leakage. Total aerosol concentration was measured inside (C(in)) and outside (C(out)) the respirator using a condensation particle counter. The total penetration through the respirator was determined as a ratio of the two (P = C(in) / C(out)). Faceseal leakage, breathing flow type and rate, and combustion material were all significant factors affecting the performance of the half mask and full facepiece respirators. The efficiency of P-100 respirator filters met the NIOSH certification criteria (penetration ≤0.03%); it was not significantly influenced by the challenge aerosol and flow type, which supports the current NIOSH testing procedure using a single challenge aerosol and a constant airflow. However, contrary to the NIOSH total inward leakage (TIL) test protocol assuming that the result is independent on the type of the tested aerosol, this study revealed that the challenge aerosol significantly affects the particle penetration through unsealed and partially sealed half mask respirators. Increasing leak size increased total particle penetration. The findings point to some limitations of the existing TIL test in predicting protection levels offered by half mask elastomeric respirators.

Background: Forty five percent of on-duty firefighter deaths every year are cardiovascular (CV) related. Heat stress and fatigue buildup are two common occupational risk factors for firefighters. These risk factors may increase the firefighters' chances of having cardiac events or even death. Objective: Buildup of heat stress and fatigue in firefighters and their recovery from these stresses during live-fire training exercises was investigated. Methods: Twenty full time firefighters, from two different fire-stations, performed live-fire training exercise constituting three real life firefighting scenarios and rest periods incorporated in between the scenarios. Core body temperature (CBT) and heart rate (HR) were measured in real time, using an FDA approved radio pill and a polar heart rate belt. Baseline and post-scenario measurements of perceptions of physical exertion, thermal stress and respiratory distress were also collected. Results: Heart rate and CBT increased significantly with the progression of the training. The HR and CBT levels at the end of each rest period were significantly higher than the baseline values. The actual rest periods provided after each scenario were shorter than the time needed for adequate recovery. Most of the firefighters crossed the industrial limit of hyperthermia and maximum recommended level of HR elevation from baseline. Firefighters from one of the stations took micro-breaks during scenarios and were found to spend less percent time over the limit of hyperthermia. These firefighters also needed less time to recover to baseline levels of HR and CBT. Conclusions: There was significant heat stress and fatigue buildup as a result of the live-fire training exercise. Longer rest periods should be provided between scenarios to ensure recovery. Also, taking micro-breaks during a live fire training scenario might help in preventing heat stress and fatigue buildup.

In this study, a novel portable ultrafine particle counter developed at the University of Cincinnati was tested against a conventional condensation particle counter (CPC, TSI Inc.) for evaluating the efficiency of respiratory protection devices. The experiments were conducted with elastomeric respirators donned on a breathing manikin using combustion particles as challenge aerosols. A favorable agreement between the two data sets on the particle penetration efficiency was observed (slope ≈ 1.16, R2 ≈ 0.99; paired t-test: p-value = 0.91), suggesting that the new counter produced meaningful data comparable to a conventional CPC instrument.

The purpose of this study was to explore data-driven models, based on decision trees, to develop practical and easy to use predictive models for early identification of firefighters who are likely to cross the threshold of hyperthermia during live-fire training. Predictive models were created for three consecutive live-fire training scenarios. The final predicted outcome was a categorical variable: will a firefighter cross the upper threshold of hyperthermia - Yes/No. Two tiers of models were built, one with and one without taking into account the outcome (whether a firefighter crossed hyperthermia or not) from the previous training scenario. First tier of models included age, baseline heart rate and core body temperature, body mass index, and duration of training scenario as predictors. The second tier of models included the outcome of the previous scenario in the prediction space, in addition to all the predictors from the first tier of models. Classification and regression trees were used independently for prediction. The response variable for the regression tree was the quantitative variable: core body temperature at the end of each scenario. The predicted quantitative variable from regression trees was compared to the upper threshold of hyperthermia (38°C) to predict whether a firefighter would enter hyperthermia. The performance of classification and regression tree models was satisfactory for the second (success rate = 79%) and third (success rate = 89%) training scenarios but not for the first (success rate = 43%). Data-driven models based on decision trees can be a useful tool for predicting physiological response without modeling the underlying physiological systems. Early prediction of heat stress coupled with proactive interventions, such as pre-cooling, can help reduce heat stress in firefighters.