Overview of the Alaskan Layered Pollution and Chemical Analysis (ALPACA) Field Experiment

The Alaskan Layered Pollution And Chemical Analysis (ALPACA) field experiment was a collaborative study designed to improve understanding of pollution sources and chemical processes during winter (cold climate and low-photochemical activity), to investigate indoor pollution, and to study dispersion of pollution as affected by frequent temperature inversions. A number of the research goals were motivated by questions raised by residents of Fairbanks, Alaska, where the study was held. This paper describes the measurement strategies and the conditions encountered during the January and February 2022 field experiment, and reports early examples of how the measurements addressed research goals, particularly those of interest to the residents. Outdoor air measurements showed high concentrations of particulate matter and pollutant gases including volatile organic carbon species. During pollution events, low winds and extremely stable atmospheric conditions trapped pollution below 73 m, an extremely shallow vertical scale. Tethered-balloon-based measurements intercepted plumes aloft, which were associated with power plant point sources through transport modeling. Because cold climate residents spend much of their time indoors, the study included an indoor air quality component, where measurements were made inside and outside a house to study infiltration and indoor sources. In the absence of indoor activities such as cooking and/or heating with a pellet stove, indoor particulate matter concentrations were lower than outdoors; however, cooking and pellet stove burns often caused higher indoor particulate matter concentrations than outdoors. The mass-normalized particulate matter oxidative potential, a health-relevant property measured here by the reactivity with dithiothreiol, of indoor particles varied by source, with cooking particles having less oxidative potential per mass than pellet stove particles

Design of multiple cause-selecting charts for multistage processes with model uncertainty

The cause-selecting chart (CSC) is an effective statistical process control tool for monitoring multistage processes. The multiple cause-selecting chart (MCSC) is the further development of the CSC, which deals with the case when the output measure is a function of multiple input measures. In practice, the model relating the input and output measures often needs to be estimated before the MCSC is implemented. However, the traditional design of MCSCs does not take parameter uncertainties into account when estimating the control limits. The actual false-alarm rate can substantially differ from what is expected. This article presents the design and implementation of MCSCs using prediction limits to account for parameter uncertainties. These limits are developed using two types of procedures: the least-squares estimation and principal component regression. The simulation results show that the prediction limits are quite effective in terms of maintaining a desired false-alarm rate