The short-term effects of air pollution on daily mortality in four Australian cities

Objective: To examine the short-term health effects of air pollution on daily mortality in four Australian cities (Brisbane, Melbourne, Perth and Sydney), where more than 50% of Australians reside. Methods: The study used a similar protocol to APHEA2 (Air Pollution and Health: A European Approach) study and derived single-city and pooled estimates. Results: The results derived from the different approaches for the 1996-99 period showed consistent results for different statistical models used. There were significant effects on total mortality, (RR=1.0284 per 1 unit increase in nelphelometry [10(-4).m(-1)], RR=1.0011 per 1ppb increase in NO2), and on respiratory mortality (RR=1.0022 per 1ppb increase in O-2). No significant differences between cities were found, but the NO2 and particle effects may refer to the same impacts. Meta-analyses carried out for three cities yielded estimates for the increase in the daily total number of deaths of 0.2% (-0.8% to 1.2%) for a 10 mu g/m(3) increase in PM, concentration, and 0.9% (-0.7% to 2.5%) for a 10 mu g/m(3) increase in PM2.5 concentration. Conclusions: Air pollutants in Australian cities have significant effects on mortality.

The short-term effects of air pollution on hospital admissions in four Australian cities

Background. This paper examines the short-term health effects of air pollution on daily hospital admissions in Australian cities (those considered comprise more than 50% of the Australian population) for the period 1996-99. Methods: The study used a similar protocol to overseas studies and derived single city and pooled estimates using different statistical approaches to assess the accuracy of the results. Results: There was little difference between the results derived from the different statistical approaches for cardiovascular admissions, while in those for respiratory admissions there were differences. For three of the four cities (for the other the results were positive but not significant), fine particles (measured by nephelometry - bsp) and nitrogen dioxide (NO2) have a significant impact on cardiovascular admissions (for total cardiac admissions, RR=1.0856 for a one-unit increase in bsp (10(-4). m(-1)), RR=1.0023 for a 1 ppb increase in NO2). For three of the four cities (for the other, the results were negative and significant), fine particles, NO2 and ozone have a significant impact on respiratory admissions (for total elderly respiratory admissions, RR=1.0552 per 1 unit (10(-4).m(-1)) increase in bsp, RR=1.0027 per 1ppb increase in NO2, RR=10014 per 1 ppb increase in ozone for elderly asthma and COPD admissions). In all analyses the particle and NO2 impacts appear to be related. Conclusions: Similar to overseas studies, air pollution has an impact on hospital admissions in Australian cities, but there can be significant differences between cities.

Cardiovascular implications of exposure to traffic air pollution during exercise

Regular aerobic exercise is recommended by physicians to improve health and longevity. However, individuals exercising in urban regions are often in contact with air pollution, which includes particles and gases associated with respiratory disease and cancer. We describe the recent evidence on the cardiovascular effects of air pollution, and the implications of exercising in polluted environments, with a view to informing clinicians and other health professionals. There is now strong evidence that fine and ultra fine particulate matter present in air pollution increases cardiovascular morbidity and mortality. The main mechanisms of disease appear to be related to an increase in the pathogenic processes associated with atherosclerosis. People exercising in environments pervaded by air contaminants are probably at increased risk, due to an exercise-induced amplification in respiratory uptake, lung deposition and toxicity of inhaled pollutants. We make evidence-based recommendations for minimizing exposure to air-borne toxins while exercising, and suggest that this advice be passed on to patients where appropriate.

An analysis of relational complexity in an air traffic control conflict detection task

Theoretical analyses of air traffic complexity were carried out using the Method for the Analysis of Relational Complexity. Twenty-two air traffic controllers examined static air traffic displays and were required to detect and resolve conflicts. Objective measures of performance included conflict detection time and accuracy. Subjective perceptions of mental workload were assessed by a complexity-sorting task and subjective ratings of the difficulty of different aspects of the task. A metric quantifying the complexity of pair-wise relations among aircraft was able to account for a substantial portion of the variance in the perceived complexity and difficulty of conflict detection problems, as well as reaction time. Other variables that influenced performance included the mean minimum separation between aircraft pairs and the amount of time that aircraft spent in conflict.

Managing the airspace: A task analysis of Australian air traffic control

The aim of this study was to examine the way Australian air traffic controllers manage their airspace. Fourteen controllers ranging from 7 to 30 years experience were sampled from the Brisbane air traffic control centre. All had previously been endorsed for en route radar sectors. Five static pictures varying in workload level (low, medium and high) were presented to participants. Controllers were asked to work through the scenarios and describe aloud how they would resolve any potential conflicts between the aircraft. Following this controllers were asked a set of probe questions based on the critical decision method, to extract further information about the way they manage their airspace. A content analysis was used to assess patterns in the way controllers scan, strategies used in conflict detection and conflict resolution and the effect of workload on strategy choice. Findings revealed that controllers use specific strategies (such as working in a left to right scan or prioritising levels) when managing their airspace. Further analyses are still planned however a model based on the processes controllers used to resolve conflicts has been developed and will be presented as a summary of the results.

Empirical evidence showing the relationships between three approaches for pollution control

Willingness to pay models have shown the theoretical relationships between the contingent valuation, cost of illness and the avertive behaviour approaches. In this paper, field survey data are used to compare the relationships between these three approaches and to demonstrate that contingent valuation bids exceed the sum of cost of illness and the avertive behaviour approach estimates. The estimates provide a validity check for CV bids and further support the claim that contingent valuation studies are theoretically consistent.

ATC-lab: An air traffic control simulator for the laboratory

Air Traffic Control Laboratory Simulator (ATC-lab) is a new low- and medium-fidelity task environment that simulates air traffic control. ATC-lab allows the researcher to study human performance of tasks under tightly controlled experimental conditions in a dynamic, spatial environment. The researcher can create standardized air traffic scenarios by manipulating a wide variety of parameters. These include temporal and spatial variables. There are two main versions of ATC-lab. The medium-fidelity simulator provides a simplified version of en route air traffic control, requiring participants to visually search a screen and both recognize and resolve conflicts so that adequate separation is maintained between all aircraft. The low-fidelity simulator presents pairs of aircraft in isolation, controlling the participant's focus of attention, which provides a more systematic measurement of conflict recognition and resolution performance. Preliminary studies have demonstrated that ATC-lab is a flexible tool for applied cognition research.

Simulation of air traffic controllers' behaviour using the operator choice model

The Operator Choice Model (OCM) was developed to model the behaviour of operators attending to complex tasks involving interdependent concurrent activities, such as in Air Traffic Control (ATC). The purpose of the OCM is to provide a flexible framework for modelling and simulation that can be used for quantitative analyses in human reliability assessment, comparison between human computer interaction (HCI) designs, and analysis of operator workload. The OCM virtual operator is essentially a cycle of four processes: Scan Classify Decide Action Perform Action. Once a cycle is complete, the operator will return to the Scan process. It is also possible to truncate a cycle and return to Scan after each of the processes. These processes are described using Continuous Time Probabilistic Automata (CTPA). The details of the probability and timing models are specific to the domain of application, and need to be specified using domain experts. We are building an application of the OCM for use in ATC. In order to develop a realistic model we are calibrating the probability and timing models that comprise each process using experimental data from a series of experiments conducted with student subjects. These experiments have identified the factors that influence perception and decision making in simplified conflict detection and resolution tasks. This paper presents an application of the OCM approach to a simple ATC conflict detection experiment. The aim is to calibrate the OCM so that its behaviour resembles that of the experimental subjects when it is challenged with the same task. Its behaviour should also interpolate when challenged with scenarios similar to those used to calibrate it. The approach illustrated here uses logistic regression to model the classifications made by the subjects. This model is fitted to the calibration data, and provides an extrapolation to classifications in scenarios outside of the calibration data. A simple strategy is used to calibrate the timing component of the model, and the results for reaction times are compared between the OCM and the student subjects. While this approach to timing does not capture the full complexity of the reaction time distribution seen in the data from the student subjects, the mean and the tail of the distributions are similar.