Operational bias correction for PM2.5 using the AIRPACT air quality forecast system in the Pacific Northwest.

A bias correction scheme based on a Kalman filter (KF) method has been developed and implemented for the AIRPACT air quality forecast system which operates daily for the Pacific Northwest. The KF method was used to correct hourly rolling 24-h average PM2.5 concentrations forecast at each monitoring site within the AIRPACT domain and the corrected forecasts were evaluated using observed daily PM2.5 24-h average concentrations from 2017 to 2018. The evaluation showed that the KF method reduced mean daily bias from approximately -50% to ±6% on a monthly averaged basis, and the corrected results also exhibited much smaller mean absolute errors typically less than 20%. These improvements were also apparent for the top 10 worst PM2.5 days during the 2017-2018 test period, including months with intensive wildfire events. Significant differences in AIRPACT performance among urban, suburban, and rural monitoring sites were greatly reduced in the KF bias correction forecasts. The daily 24-h average bias corrections for each monitoring site were interpolated to model grid points using three different interpolation schemes: cubic spline, Gaussian Kriging, and linear Kriging. The interpolated results were more accurate than the original AIRPACT forecasts, and both Kriging methods were better than the cubic spline method. The Gaussian method yielded smaller mean biases and the linear method yielded smaller absolute errors. The KF bias correction method has been implemented operationally using both Kriging interpolation methods for routine output on the AIRPACT website (http://lar.wsu.edu/airpact). This method is relatively easy to implement, but very effective to improve air quality forecast performance.Implications: Current chemical transport models, including CMAQ, used for air quality forecasting can have large errors and uncertainties in simulated PM2.5 concentrations. In this paper, we describe a relatively simple bias correction scheme applied to the AIRPACT air quality forecast system for the Pacific Northwest. The bias correction yields much more accurate and reliable PM2.5 results compared to the normal forecast system. As such, the operational bias corrected forecasts will provide a much better basis for daily air quality management by agencies within the region. The bias corrected results also highlight issues to guide further improvements to the normal forecast system.

An End-of-Life Care Educational Series to Improve Staff Knowledge and Comfort Levels.

Nursing staff play a key role in enhancing a patient's quality of life during end of life; however, they perceive lack of knowledge to be the largest barrier in providing quality end-of-life (EOL) care. Literature suggests that implementation of palliative and EOL care education can improve nursing EOL care practices. In order to address the gap in nursing knowledge and comfort, a quasi-experimental study was conducted; this study included the implementation of a multimodal EOL care educational series on an inpatient pediatric hematology oncology floor over 6 months. Prior to implementation, nursing staff completed a survey to measure perceived knowledge and comfort level regarding EOL care. The series included didactic sessions, in-services, case studies, practice exercises, and interactive discussions led by an interprofessional team of nurses, child-life specialists, and social workers. Educational topics included EOL symptom management, child-life services, supportive care resources, COMFORT communication, and an End-of-Life Nursing Education Consortium course. Following the educational series, the survey was repeated. Results of the survey demonstrated an increase in nursing knowledge and comfort levels. Significant improvements were observed across several items including medication management of dyspnea (χ1,83 =5.1, P = .023), comfort with implementing interventions (χ1,93 = 3.9, P = .049), and knowledge of hospital resources (χ1,93 = 6.1, P = .014). These results suggest that while EOL education strategies can vary, a combination of learner engagement tactics can increase knowledge and comfort regarding EOL concepts and potentially positively impact nursing practice.

Spatial and temporal variability of brown carbon in United States: implications for direct radiative effects.

A newly developed dataset from the Interagency Monitoring of PROtected Visual Environments (IMPROVE) observation network, combined with a 3-D chemical transport model, is used to evaluate the spatial and temporal variability of brown carbon (BrC) in the United States. The model with BrC emitted from biomass burning and biofuel emissions agrees with the seasonal and spatial variability of BrC planetary boundary layer (PBL) absorption aerosol optical depth (AAOD) observations within a factor of 2. The model without whitening, the tendency for absorption to decrease with aerosol aging, overestimates the observed BrC PBL AAOD, and does not reflect the measured BrC PBL AAOD spatial variability. The model shows higher absorption direct radiative effects (DRE) from BrC at northern high latitudes than at mid-latitudes in spring and summer, due to boreal fire emissions, long whitening lifetimes and high surface albedos. These findings highlight the need to study BrC over the Arctic region.