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Case-crossover designs have been widely applied to epidemiological and medical investigations of associations between short-term exposures and risk of acute adverse health events. Much effort has been made in literature on understanding source of confounding and reducing systematic bias by reference-select strategies. In this paper, we explored the nature of bias in the ambi-directional and time-stratified case-crossover designs via simulation using actual air pollution data from urban Edmonton, Alberta, Canada. We further proposed a calibration approach for eliminating systematic bias in estimates (coefficient estimate, 95% confident interval, and p-value). Bias check for coefficient estimation, size check and power check for significance test were done via simulation experiments to show advantages of the calibrated case-crossover studies over the ones without calibration. An application was done to investigate associations between air pollutants and acute myocardial infarction hospitalizations in urban Edmonton. In conclusion, systematic bias in a case-crossover design is often unavoidable, leading to an obvious bias in the estimated effect and an unreliable p value in the significance test. The proposed calibration technique provides an efficient approach to eliminating systematic bias in a case-crossover study. [ABSTRACT FROM AUTHOR]

Top-down approaches to measure total integrated emissions provide verification of bottom-up, temporally-resolved, inventory-based estimations. Aircraft-based measurements of air pollutants from sources in the Canadian oil sands were made in support of the Joint Canada-Alberta Implementation Plan on Oil Sands Monitoring during a summer intensive field campaign between 13 August and 7 September 2013. The measurements contribute to knowledge needed in support of the Joint Canada-Alberta Implementation Plan on Oil Sands Monitoring. This paper describes a Top-down Emission Rate Retrieval Algorithm (TERRA) to determine facility emissions of pollutants, using SO2 and CH4 as examples, based on the aircraft measurements. In this algorithm, the flight path around a facility at multiple heights is mapped to a two-dimensional vertical screen surrounding the facility. The total transport of SO2 and CH4 through this screen is calculated using aircraft wind measurements, and facility emissions are then calculated based on the divergence theorem with estimations of box-top losses, hori zontal and vertical turbulent fluxes, surface deposition, and apparent losses due to air densification and chemical reaction. Example calculations for two separate flights are presented. During an upset condition of SO2 emissions on one day, these calculations are within 5% of the industry-reported, bottom-up measurements. During a return to normal operating conditions, the SO2 emissions are within 11% of industry-reported, bottom-up measurements. CH4 emissions calculated with the algorithm are relatively constant within the range of uncertainties. Uncertainty of the emission rates is estimated as 20 %, which is primarily due to the unknown SO2 and CH4 mixing ratios near the surface below the lowest flight level. [ABSTRACT FROM AUTHOR]