Development and validation of a multilevel model for predicting workload under routine and nonroutine conditions in an air traffic management center

Objective: The aim of this study was to develop a model capable of predicting variability in the mental workload experienced by frontline operators under routine and nonroutine conditions. Background: Excess workload is a risk that needs to be managed in safety-critical industries. Predictive models are needed to manage this risk effectively yet are difficult to develop. Much of the difficulty stems from the fact that workload prediction is a multilevel problem. Method: A multilevel workload model was developed in Study 1 with data collected from an en route air traffic management center. Dynamic density metrics were used to predict variability in workload within and between work units while controlling for variability among raters. The model was cross-validated in Studies 2 and 3 with the use of a high-fidelity simulator. Results: Reported workload generally remained within the bounds of the 90% prediction interval in Studies 2 and 3. Workload crossed the upper bound of the prediction interval only under nonroutine conditions. Qualitative analyses suggest that nonroutine events caused workload to cross the upper bound of the prediction interval because the controllers could not manage their workload strategically. Conclusion: The model performed well under both routine and nonroutine conditions and over different patterns of workload variation. Application: Workload prediction models can be used to support both strategic and tactical workload management. Strategic uses include the analysis of historical and projected workflows and the assessment of staffing needs. Tactical uses include the dynamic reallocation of resources to meet changes in demand.

Characterisation of the impact of open biomass burning on urban air quality in Brisbane, Australia

Open biomass burning from wildfires and the prescribed burning of forests and farmland is a frequent occurrence in South-East Queensland (SEQ), Australia. This work reports on data collected from 10-30 September 2011, which covers the days before (10-14 September), during (15-20 September) and after (21-30 September) a period of biomass burning in SEQ. The aim of this project was to comprehensively quantify the impact of the biomass burning on air quality in Brisbane, the capital city of Queensland. A multi-parameter field measurement campaign was conducted and ambient air quality data from 13 monitoring stations across SEQ were analysed. During the burning period, the average concentrations of all measured pollutants increased (from 20% to 430%) compared to the non-burning period (both before and after burning), except for total xylenes. The average concentration of O3, NO2, SO2, benzene, formaldehyde, PM10, PM2.5 and visibility-reducing particles reached their highest levels for the year, which were up to 10 times higher than annual average levels, while PM10, PM2.5 and SO2 concentrations exceeded the WHO 24-hour guidelines and O3 concentration exceeded the WHO maximum 8-hour average threshold during the burning period. Overall spatial variations showed that all measured pollutants, with the exception of O3, were closer to spatial homogeneity during the burning compared to the non-burning period. In addition to the above, elevated concentrations of three biomass burning organic tracers (levoglucosan, mannosan and galactosan), together with the amount of non-refractory organic particles (PM1) and the average value of f60 (attributed to levoglucosan), reinforce that elevated pollutant concentration levels were due to emissions from open biomass burning events, 70% of which were prescribed burning events. This study, which is the first and most comprehensive of its kind in Australia, provides quantitative evidence of the significant impact of open biomass burning events, especially prescribed burning, on urban air quality. The current results provide a solid platform for more detailed health and modelling investigations in the future.

Urban growth dilemmas and solutions in China: Looking forward to 2030

China’s urbanization and industrialization are occupying farmland in large amounts, which is strongly driven by land finance regime. This is due to the intensified regional/local competition for manufacturing investment opportunities that push local governments to expropriate farmland at low prices while lease land at high market value to property developers. The additional revenue obtained in this way, termed financial increment in land values, can drive local economic growth, and provide associated infrastructure and other public services. At the same time, however, a floating population of large numbers of inadequately compensated land-lost farmers, although unable to become citizens, have to migrate into the urban areas for work, causing overheated employment and housing markets, with rocketing unaffordable housing prices. This, together with various micro factors relating to the party/state’s promotion/evaluation system play an essential role leading to some serious economic, environment and social consequences, e.g., on migrant welfare, the displacement of peasants and the loss of land resources that requires immediate attention. Our question is: whether such type of urbanization is sustainable? What are the mechanisms behind such a phenomenal urbanization process? From the perspective of institutionalism, this paper aims to investigate the institutional background of the urban growth dilemma and solutions in urban China and to introduce further an inter-regional game theoretical framework to indicate why the present urbanization pattern is unsustainable. Looking forward to 2030, paradigm policy changes are made from the triple consideration of floating population, social security and urban environmental pressures. This involves: (1) changing land increment based finance regime into land stock finance system; (2) the citizenization of migrant workers with affordable housing, and; (3) creating a more enlightened local government officer appraisal system to better take into account societal issues such as welfare and beyond.