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This study examined the impacts of aircraft emissions during the landing and takeoff cycle on PM_2.5 concentrations during the months of June and July 2002 at the Hartsfield–Jackson Atlanta International Airport. Primary and secondary pollutants were modeled using the Advanced Modeling System for Transport, Emissions, Reactions, and Deposition of Atmospheric Matter (AMSTERDAM). AMSTERDAM is a modified version of the Community Multiscale Air Quality (CMAQ) model that incorporates a plume-in-grid process to simulate emissions sources of interest at a finer scale than can be achieved using CMAQ's model grid. Three fundamental issues were investigated: the effects of aircraft on PM_2.5 concentrations throughout northern Georgia, the differences resulting from use of AMSTERDAM's plume-in-grid process rather than a traditional CMAQ simulation, and the concentrations observed in aircraft plumes at subgrid scales. Comparison of model results with an air quality monitor located in the vicinity of the airport found that normalized mean bias ranges from −77.5% to 6.2% and normalized mean error ranges from 40.4% to 77.5%, varying by species. Aircraft influence average PM_2.5 concentrations by up to 0.232 μg m⁻³ near the airport and by 0.001–0.007 μg m⁻³ throughout the Atlanta metro area. The plume-in-grid process increases concentrations of secondary PM pollutants by 0.005–0.020 μg m⁻³ (compared to the traditional grid-based treatment) but reduces the concentration of non-reactive primary PM pollutants by up to 0.010 μg m⁻³, with changes concentrated near the airport. Examination of subgrid-scale results indicates that median aircraft contribution to grid cells is higher than median puff concentration in the airport's grid cell and outside of a 20 km × 20 km square area centered on the airport, while in a 12 km × 12 km square ring centered on the airport, puffs have median concentrations over an order of magnitude higher than aircraft contribution to the grid cells. Maximum puff impacts are seen within the 12 km × 12 km ring, not in the airport's own grid cell, while maximum grid cell impacts occur within the airport's grid cell. Twenty-one (21)% of all aircraft-related puffs from the Atlanta airport have at least 0.1 μg m⁻³ PM_2.5 concentrations. Near the airport, median daily puff concentrations vary between 0.017 and 0.134 μg m⁻³ (0.05 and 0.35 μg m⁻³ at ground level), while maximum daily puff concentrations vary between 6.1 and 42.1 μg m⁻³ (7.5 and 42.1 μg m⁻³ at ground level) during the 2-month period. In contrast, median daily aircraft contribution to grid concentrations varies between 0.015 and 0.091 μg m⁻³ (0.09 and 0.40 μg m⁻³ at ground level), while the maximum varies between 0.75 and 2.55 μg m⁻³ (0.75 and 2.0 μg m⁻³ at ground level). Future researchers may consider using a plume-in-grid process, such as the one used here, to understan...

Many uncertainties in Material Requirements Planning (MRP) systems are treated as “controllable” elements, with a variety of buffering, dampening and other approaches being used to cope with them. However, such approaches are often found wanting, forcing enterprises into emergency measures to ensure delivery performance. Based upon the results of a questionnaire survey, this paper analyses the uncertainties in the aggregate, intermediate and operational levels that affect customer delivery performance in MRP environments. These uncertainties have been quantified and the relative importance to performance has been investigated. The results also show the widespread use of buffering, dampening and other approaches to provide a level of delivery reliability. It is contended that by concentrating on minimising the effects of the uncertainties, the underlying causes have not been addressed, resulting in sub‐optimisation of system performance.

This article reports on the first implementation of a real-time Eulerian photochemical model f o recast system in the United States. The forecast system consists of a tripartite set of one-way coupled models that run routinely on a parallel micro process or supercomputer. The component models are the fifth-generation Pennsylvania State University (PSU)–NCAR Mesoscale Model (MM5), the Sparse-Matrix Operator Kernel for Emissions (SMOKE) model, and the Multiscale Air Quality Simulation Platform—Real Time (MAQSIPRT) photochemical model. Though the system has been run in real time since the summer of 1998, forecast results obtained during August of 2001 at 15-km grid spacing over New England and the northern mid-Atlantic—conducted as part of an “early start” NOAA air quality forecasting initiative—are described in this article.The development and deployment of a real-time numerical air quality prediction (NAQP) system is technically challenging. MAQSIP-RT contains a full photochemical oxidant gas-phase chemical mechanism together with transport, dry deposition, and sophisticated cloud treatment. To enable the NAQP system to run fast enough to meet operational forecast deadlines, significant work was devoted to data flow design and software engineering of the models and control codes. The result is a turnkey system now in use by a number of agencies concerned with operational ozone forecasting.Results of the chosen episode are compared against three other models/modeling techniques: a traditional statistical model used routinely in the metropolitan Philadelphia, Pennsylvania, area, a set of publicly issued forecasts in the northeastern United States, and the operational Canadian Hemispheric and Regional Ozone and NOx System (CHRONOS) model. For the test period it is shown that the NAQP system performs as well or better than all of these operational approaches. Implications for the impending development of an operational U.S. ozone forecasting capability are discussed in light of these results.