Spatial and temporal variability in CH4 and N2O fluxes from a Scottish ombrotrophic peatland: implications for modeling and upscaling

Peatlands typically exhibit significant spatial heterogeneity which can lead to large uncertainties when catchment scale greenhouse gas fluxes are extrapolated from chamber measurements (generally <1 m2). Here we examined the underlying environmental and vegetation characteristics which led to within-site variability in both CH4 and N2O emissions and the importance of such variability in up-scaling. We also consider within-site variation in the controls of temporal dynamics. Net annual emissions (and coefficients of variation) for CH4 and N2O were 1.06 kg ha−1 y−1 (300%) and 0.02 kg ha−1 y−1 (410%), respectively. The riparian zone was a significant CH4 hotspot contributing 12% of the total catchment emissions whilst covering only 0.5% of the catchment area. In contrast to many other studies we found smaller CH4 emissions and greater uptake in chambers containing either sedges or rushes. We also found clear differences in the drivers of temporal CH4 dynamics across the site, e.g. water table was important only in chambers which did not contain aerenchymous plants. We suggest that depending on the heterogeneity of the site, flux models could be improved by incorporating a number of spatially distinct sub-models, rather than a single model parameterized using whole-catchment averages

Spatial variability of methane emissions from Swiss alpine fens

ISSN:0923-4861ISSN:1572-983

Corrigendum to::More Than Smell-COVID-19 Is Associated with Severe Impairment of Smell, Taste, and Chemesthesis (Chemical Senses (2020) DOI: 10.1093/chemse/bjaa041)

This is a correction notice for article bjaa041 (DOI: https:// doi.org/10.1093/chemse/bjaa041), published 20 June 2020. An incorrect version of the caption to Figure 5 was mistakenly included in the published paper. An updated version is given below. Neither the data nor the paper's conclusions were affected by this correction. The authors sincerely apologize for the error. (A) Correlations between the 3 principal components with respect to changes in 3 chemosensory modalities (i.e., taste, smell, and chemesthesis). Shades of gray indicate positive correlation, whereas shades of red indicate negative correlations. White denotes no correlation. (B) Clusters of participants identified by k-means clustering. The scatterplot shows each participant's loading on dimension 1 (degree of smell and taste loss, PC1 on x-Axis) and dimension 2 (degree of chemesthesis loss, PC2 on y-Axis). Based on the centroid of each cluster, participants in cluster 1 (blue, N = 1767; top left) are generally characterized by significant smell, taste and chemesthesis loss. Participants in cluster 2 (orange, N = 1724; bottom center) are generally characterized by ratings that reflect smell/taste loss with preserved chemesthesis. Loadings for participants in cluster 3 (green, N = 548; right side) are generally characterized by reduced smell and taste loss, and preserved chemesthesis

The best COVID-19 predictor is recent smell loss: a cross-sectional study

Background: COVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19. Methods: This preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery. Results: Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing no significant model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ~50% of participants and was best predicted by time since illness onset. Conclusions: As smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (10<OR<4), especially when viral lab tests are impractical or unavailable