[1] Arctic regional climate is influenced by the radiative impact of aerosol black carbon (BC) both in the atmosphere and deposited on the snow and ice covered surfaces. The NIES (National Institute for Environmental Studies) global atmospheric transport model was used, with BC emissions from mid-latitude fossil fuel and biomass burning source regions, to simulate BC concentrations with 16 year period. The model-simulated BC agreed well with the BC observations, including the trends and seasonality, at three Arctic sites: Alert (Nunavut, Canada), Barrow (Alaska, USA), and Zepplin, Ny-Ålesund (Svalbard, Norway). The equivalent black carbon (EBC, absorption inferred BC) observations at the three Arctic locations showed an overall decline of 40% from 1990 to 2009; with most change occurring during early 1990s. Model simulations confirmed declining influence on near surface BC contribution by 70% , and atmospheric BC burden by one half from the Former Soviet Union (FSU) BC source region over 16 years. In contrast, the BC contribution from the East Asia (EA) region has little influence at the surface but atmospheric Arctic BC burden increased by 3 folds. Modelled dry deposition is dominant in the Arctic during wintertime, while wet deposition prevails at all latitudes during summer. Sensitivity analyses on the dry and wet deposition schemes indicate that parameterizations need to be refined to improve on the model performance. There are limitations in the model due to simplified parameterizations and remaining model uncertainties, which requires further exploration of source region contributions, especially from growing EA source region to Arctic BC levels in the future is warranted.

[1] Sunlit and shaded leaf separation proposed by Norman (1982) is an effective way to upscale from leaf to canopy in modeling vegetation photosynthesis. The Boreal Ecosystem Productivity Simulator (BEPS) makes use of this methodology, and has been shown to be reliable in modeling the gross primary productivity (GPP) derived from CO 2 flux and tree ring measurements. In this study, we use BEPS to investigate the effect of canopy architecture on the global distribution of GPP. For this purpose, we use not only leaf area index (LAI) but also the first ever global map of the foliage clumping index derived from the multiangle satellite sensor POLDER at 6 km resolution. The clumping index, which characterizes the degree of the deviation of 3-dimensional leaf spatial distributions from the random case, is used to separate sunlit and shaded LAI values for a given LAI. Our model results show that global GPP in 2003 was 132 AE 22 Pg C. Relative to this baseline case, our results also show: (1) global GPP is overestimated by 12% when accurate LAI is available but clumping is ignored, and (2) global GPP is underestimated by 9% when the effective LAI is available and clumping is ignored. The clumping effects in both cases are statistically significant (p < 0.001). The effective LAI is often derived from remote sensing by inverting the measured canopy gap fraction to LAI without considering the clumping. Global GPP would therefore be generally underestimated when remotely sensed LAI (actually effective LAI by our definition) is used. This is due to the underestimation of the shaded LAI and therefore the contribution of shaded leaves to GPP. We found that shaded leaves contribute 50%, 38%, 37%, 39%, 26%, 29% and 21% to the total GPP for broadleaf evergreen forest, broadleaf deciduous forest, evergreen conifer forest, deciduous conifer forest, shrub, C4 vegetation, and other vegetation, respectively. The global average of this ratio is 35%.

Background Vancomycin area under the concentration‐time curve (AUC) has been linked to efficacy and safety. An accurate method of calculating the AUC is needed. Methods Bayesian dose‐optimizing software programs available for clinician use and first‐order pharmacokinetic equations were evaluated for their ability to estimate vancomycin AUC. A previously published rich pharmacokinetic data set of 19 critically ill patients was used for validation of the AUC estimation. The AUC estimated using subsets of the full data set by Bayesian software and equations was compared with the reference AUC. Accuracy (ratio of estimated AUC to the reference AUC) and bias (percentage difference of estimated AUC to reference AUC) were calculated. Results Five Bayesian dose‐optimizing software programs (Adult and Pediatric Kinetics [APK], BestDose, DoseMe, InsightRx, and Precise PK) and two first‐order pharmacokinetic equations were included. Of the Bayesian programs, InsightRx was the most adaptable, visually appealing, easiest to use, and had the most company support. Utilizing only the trough, accuracy (range 0.79–1.03) and bias (range 5.1–21.2%) of the Bayesian dose‐optimizing software were variable. Precise PK and BestDose had the most accurate estimates with the accuracy values of BestDose exhibiting the most variability of all the programs; however, both programs were more difficult to use. Precise PK was the least biased (median 5.1%). Using a single nontrough value produced similar results to that of the trough for most programs. The addition of a second level to the trough improved the accuracy and bias for DoseMe and InsightRx but not Precise PK and BestDose. APK did not reliably estimate the AUC with input of two levels. Using two levels, the pharmacokinetic equations produced similar or better accuracy and bias as compared with Bayesian software. Conclusion Bayesian dose‐optimizing software using one or more vancomycin levels and pharmacokinetic equations utilizing two vancomycin levels produce similar estimates of the AUC.

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The provinces of Alberta and Saskatchewan account for 70% of Canada's methane emissions from the oil and gas sector. In 2018, the Government of Canada introduced methane regulations to reduce emissions from the sector by 40−45% from the 2012 levels by 2025. Complementary to inventory accounting methods, the effectiveness of regulatory practices to reduce emissions can be assessed using atmospheric measurements and inverse models. Total anthropogenic (oil and gas, agriculture, and waste) emission rates of methane from 2010 to 2017 in Alberta and Saskatchewan were derived using hourly atmospheric methane measurements over a sixmonth winter period from October to March. Scaling up the winter estimate to annual indicated an anthropogenic emission rate of 3.7 ± 0.7 MtCH 4 /year, about 60% greater than that reported in Canada's National Inventory Report (2.3 MtCH 4 ). This discrepancy is tied primarily to the oil and gas sector emissions as the reported emissions from livestock operations (0.6 MtCH 4 ) are well substantiated in both topdown and bottom-up estimates and waste management (0.1 MtCH 4 ) emissions are small. The resulting estimate of 3.0 MtCH 4 from the oil and gas sector is nearly twice that reported in Canada's National Inventory (1.6 MtCH 4 ).