Suchergebnisse

The success of driver graduated licensing systems (GLS) is demonstrated primarily in jurisdictions that licence at young ages with requirements expiring at age 18. In Australia, GLS requirements typically apply for all applicants aged under 25. In 2007, the Queensland licensing system was strengthened, extending the learner and introducing a 100-hour supervised driving requirement, introducing restrictions on passenger carriage at night and high-powered vehicles for provisional drivers, and on phone use for all novice drivers (learner and provisional). The objective of the current research was to evaluate whether these changes were associated with reductions in crashes (all) and killed-and-serious-injury (KSI) crashes involving novice drivers, and respective casualties. Government licensing and police crash records were linked and interrupted time series analysis was used to examine potential shifts in crash trends by rates of licensed drivers per month. Substantial declines were found in novice driver crashes (13.1% per year; 95%CI -0.0130, -0.0096), crash casualties (13.9% per year; 95%CI -0.0137, -0.0101), KSI crashes (5.4% per year; 95%CI -0.0080, -0.0046) and associated casualties (5.2% per year; 95%CI -0.0075, -0.0039). Compared to the total licensed driver population, declines in crashes (3.0% per year; 95%CI -0.0027, -0.0007) and crash casualties (2.9% per year; 95%CI -0.0029, -0.0006) but not KSI outcomes were observed. More narrowly, declines were found for provisional- licensed driver crashes (9.3% per year; 95%CI -0.0096, -0.0063) and KSI crashes (3.6% per year; 95%CI -0.0004, -0.0128) that were approximately 2.6% and 1.2% greater than respective declines for 25-29-year-old open-licensed drivers. Substantial declines also were observed in novice driver single-vehicle, night, passenger and alcohol crashes. Overall, these results demonstrate that GLS can be effective in a later age licensing jurisdiction. However, KSI outcomes were limited. Modelling research is recommended on ways to further strengthen Queensland's GLS to achieve greater trauma reductions.

Objectives - To qualitatively explore barriers to optimal child restraint use using the integrative behaviour change model in culturally and linguistically diverse (CALD) communities in New South Wales (NSW), Australia. Methods - A semi-structured discussion was used to conduct 11 language specific focus groups in Arabic, Assyrian, Cantonese, Mandarin, Vietnamese and Turkish. Translated transcriptions were analysed using the major concepts of the integrative behaviour change model.Results - Restraint use intent among CALD community carers is related to perceived safety of their children and complying with the law. While most participants appreciated the safety benefits of correct and appropriate use, a minority did not. Child restraint legislation may positively influence social norms, and enforcement appears to increase parental self-efficacy. However, concerns over child comfort may negatively influence both norms and self-efficacy. There are clear deficits in knowledge that may act as barriers as well as confusion over best practice in safely transporting children. Large family size, vehicle size and cost appear to be real environmental constraints in CALD communities.Conclusion - Determinants of intent and deficits in knowledge in this diverse range of CALD communities in NSW Australia are similar to those reported in other qualitative studies regardless of the population studied. This indicates that key messages should be the same regardless of the target population. However, for CALD communities there is a specific need to ensure access to detailed information through appropriate delivery strategies and languages. Furthermore, practical constraints such as cost of restraints and family size may be particularly important in CALD communities.

Particulate matter (PM) emissions from agricultural operations are an important issue for air quality and human health and a topic of interest to government regulators. PM emission rates from a dairy in the San Joaquin Valley of California were investigated during June 2008. The facility had 1,885 total animals, including 950 milking cows housed in free‐stall pens with an open‐lot exercise area, and 935 dry cows, steers, bulls, and heifers housed in open lots. Point sensors, including filter‐based aerodynamic mass samplers and optical particle counters (OPC), were deployed at select points around the facility to measure optical and aerodynamic particulate concentrations. Simultaneously, vertical PM concentration profiles were measured both upwind and downwind of the facility using lidar. The lidar was calibrated to provide mass concentration information using the OPCs and filter measurements. Emission rates were estimated over this period using both an inverse modeling technique coupled with the filter‐based measurements and a mass‐balance technique applied to lidar data. Mean emission rates calculated using inverse modeling (±95% confidence interval) were 3.8 (±3.2), 24.8 (±14.5), and 75.9 (±33.2) g d‐1 AU‐1 for PM2.5, PM10, and TSP, respectively. Mean emissions rates based on lidar data were 1.3 (±0.2), 15.1 (±2.2), and 46.4 (±7.0) g d‐1 AU‐1 for PM2.5, PM10, and TSP, respectively. The PM10 findings are roughly twice as high as those reported from other dairy studies with different climatic conditions and/or housing types, but are of similar magnitude as those from a study with similar conditions, housing, and emission rate calculation technique.

Particulate matter (PM) emissions from agricultural operations are an important issue for air quality and human health and a topic of interest to government regulators. PM emission rates from a dairy in the San Joaquin Valley of California were investigated during June 2008. The facility had 1,885 total animals, including 950 milking cows housed in free-stall pens with an open-lot exercise area, and 935 dry cows, steers, bulls, and heifers housed in open lots. Point sensors, including filter-based aerodynamic mass samplers and optical particle counters (OPC), were deployed at select points around the facility to measure optical and aerodynamic particulate concentrations. Simultaneously, vertical PM concentration profiles were measured both upwind and downwind of the facility using lidar. The lidar was calibrated to provide mass concentration information using the OPCs and filter measurements. Emission rates were estimated over this period using both an inverse modeling technique coupled with the filter-based measurements and a mass-balance technique applied to lidar data. Mean emission rates calculated using inverse modeling ( ± 95% confidence interval) were 3.8 ( ± 3.2), 24.8 ( ± 14.5), and 75.9 ( ± 33.2) g d -1 AU -1 for PM 2.5 , PM 10 , and TSP, respectively. Mean emissions rates based on lidar data were 1.3 ( ± 0.2), 15.1 ( ± 2.2), and 46.4 ( ± 7.0) g d -1 AU -1 for PM 2.5 , PM 10 , and TSP, respectively. The PM 10 findings are roughly twice as high as those reported from other dairy studies with different climatic conditions and/or housing types, but are of similar magnitude as those from a study with similar conditions, housing, and emission rate calculation technique.

Ground based remote sensing technologies such as scanning lidar systems (light detection and ranging) are increasingly being used to characterize ambient aerosols due to key advantages (i.e., wide area of regard (10 km2), fast response time, high spatial resolution (<10 m) and high sensitivity). Scanning lidar allows for 3D imaging of atmospheric motion and aerosol variability. Space Dynamics Laboratory at Utah State University, in conjunction with the USDA-ARS, has developed and successfully deployed a three-wavelength lidar system called Aglite to characterize particles in diverse settings. Aglite generates near real-time imagery of particle size distribution and size-segregated mass concentration in addition to the ability to calculate whole facility emission rates. Based on over nine years of field and laboratory experience, we present concentration and emission rate results from various measurements in military and civilian deployments.

Preparation of soil for agricultural crops produces aerosols that may significantly contribute to seasonal atmospheric loadings, especially in areas with a high density of perennial crops. Emissions may originate from the tractor's diesel engine, the tractor moving over the ground, and the equipment used for tillage. The United States (US) Environmental Protection Agency (EPA) and some state governments are now including more agricultural air pollutant emissions in particulate matter (PM) emissions inventories. However, reductions in tillage PM emissions from traditional practices through the use of conservation management practices (CMPs) have often not been quantified, especially for PM2.5. Potential CMP options include, but are not limited to, no till methods, strip till methods, and methods that reduce the number of operations used. A study was conducted in the Central Valley of California in October 2007 to measure PM emissions from the conventional method of preparing the ground after cotton harvest and going back into cotton the following spring. We also examined a "Combined Operations" method, which combines tillage implements in order to reduce the number of passes in the field, in an adjacent field with an identical cropping system for comparison. Two adjacent fields near Los Banos, CA were selected for this study based on geometry, predominant wind direction, operator cooperation, and similar soil types. Measurements made include: soil properties, meteorological (T, RH, WS, and WD) profiles, filter-based TSP, PM10, and PM2.5 concentrations and aerosol size distribution via optical particle counters at multiple up- and downwind locations and heights, and a calibrated, scanning, three wavelength lidar. Emission rates were determined via 1) inverse modeling (ISCST3 and AERMOD) coupled with the mass concentration measurements and 2) application of a mass balance to the virtual box created around the operation by the scanning lidar system. Emission rates from both conventional and combined operations methods will be presented and compared.

The Advancing Care Coordination & Telehealth Deployment (ACT) Programme is the first to explore the organisational and structural processes needed to successfully implement care coordination and telehealth (CC&TH) services on a large scale. A number of insights and conclusions were identified by the ACT programme. These will prove useful and valuable in supporting the large-scale deployment of CC&TH. Targeted at populations of chronic patients and elderly people, these insights and conclusions are a useful benchmark for implementing and exchanging best practices across the EU. Examples are: Perceptions between managers, frontline staff and patients do not always match; Organisational structure does influence the views and experiences of patients: a dedicated contact person is considered both important and helpful; Successful patient adherence happens when staff are engaged; There is a willingness by patients to participate in healthcare programmes; Patients overestimate their level of knowledge and adherence behaviour; The responsibility for adherence must be shared between patients and health care providers; Awareness of the adherence concept is an important factor for adherence promotion; The ability to track the use of resources is a useful feature of a stratification strategy, however, current regional case finding tools are difficult to benchmark and evaluate; Data availability and homogeneity are the biggest challenges when evaluating the performance of the programmes. ; European Union