On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.     

Methane and nitrous oxide emissions from a subtropical coastal embayment (Moreton Bay, Australia)

Surface water methane (CH₄) and nitrous oxide (N₂O) concentrations and fluxes were investigated in two subtropical coastal embayments (Bramble Bay and Deception Bay, which are part of the greater Moreton Bay, Australia). Measurements were done at 23 stations in seven campaigns covering different seasons during 2010-2012. Water-air fluxes were estimated using the Thin Boundary Layer approach with a combination of wind and currents-based models for the estimation of the gas transfer velocities. The two bays were strong sources of both CH₄ and N₂O with no significant differences in the degree of saturation of both gases between them during all measurement campaigns. Both CH₄ and N₂O concentrations had strong temporal but minimal spatial variability in both bays. During the seven seasons, CH₄ varied between 500% and 4000% saturation while N₂O varied between 128 and 255% in the two bays. Average seasonal CH₄ fluxes for the two bays varied between 0.5 ± 0.2 and 6.0 ± 1.5 mg CH₄/(m²·day) while N₂O varied between 0.4 ± 0.1 and 1.6 ± 0.6 mg N₂O/(m²·day). Weighted emissions (t CO₂-e) were 63%-90% N₂O dominated implying that a reduction in N₂O inputs and/or nitrogen availability in the bays may significantly reduce the bays' greenhouse gas (GHG) budget. Emissions data for tropical and subtropical systems is still scarce. This work found subtropical bays to be significant aquatic sources of both CH₄ and N₂O and puts the estimated fluxes into the global context with measurements done from other climatic regions.