Search

Introduction Lead exposure reduction has been one of the most successful environmental health campaigns ever undertaken. (1) However, for the target goal of 'zero' micrograms of lead in blood to [...]

Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.

The Amazon forest is the largest source of isoprene emissions, and the seasonal pattern of leaf-out phenology in this forest has been indicated as an important driver of seasonal variation in emissions. Still, it is unclear how emissions vary between different leaf phenological types in this forest. To evaluate the influence of leaf phenological type over isoprene emissions, we measured leaf-level isoprene emission capacity and leaf functional traits for 175 trees from 124 species of angiosperms distributed among brevideciduous and evergreen trees in a central Amazon forest. Evergreen isoprene emitters were less likely to store monoterpenes and had tougher and less photosynthetically active leaves with higher carbon-to-nitrogen ratios compared to non-emitters. Isoprene emission rates in brevideciduous trees were higher with a higher diversity of stored sesquiterpenes and total phenolics content. Our results suggest that the way isoprene emissions relate to growth and defense traits in central Amazon trees might be influenced by leaf phenological type, and that isoprene may participate in co-regulating a chemical-mechanical defense trade-off between brevideciduous and evergreen trees. Such knowledge can be used to improve emission estimates based on leaf phenological type since, as a highly-emitted biogenic volatile organic compound (BVOC), isoprene affects atmospheric processes with implications for the Earth's radiative balance. [ABSTRACT FROM AUTHOR]

In mid-January 2005 a convective squall line traversed 4.5 × 106 km2 of Amazonia from southwest to northeast. As seen in Landsat images, this atypical convective storm left blowdown imprints with diffuse geometry, unlike the fan-shaped wind disturbance of much more frequent east-to-west propagating squall lines. Previous work reported 0.2% of the forest area damaged by this one relatively rare event within one Landsat image and assumed similar disturbance across the entire traverse. We mapped convective wind damage impact to the region in 2005 by identifying large-scale (>4 ha) blowdown imprints in 30 Landsat images. The diffuse-type imprints associated with this single squall line contributed up to 60–72% of total 2005 wind-disturbed area detected across the region, but damage was highly concentrated in central Amazonia. Consequently, the distribution of large wind damage patches in 2005 across Amazonia was very different from long-term average. Regional distribution of wind-driven tree mortality for smaller patch sizes remains unknown.

Tropical rainforests are an important source of isoprenoid and other volatile organic compound (VOC) emissions to the atmosphere. The seasonal variation of these compounds is however still poorly understood. In this study, vertical profiles of mixing ratios of isoprene, total monoterpenes and total sesquiterpenes, were measured within and above the canopy, in a primary rainforest in central Amazonia, using a proton transfer reaction - mass spectrometer (PTR-MS). Fluxes of these compounds from the canopy into the atmosphere were estimated from PTR-MS measurements by using an inverse Lagrangian transport model. Measurements were carried out continuously from September 2010 to January 2011, encompassing the dry and wet seasons. Mixing ratios were higher during the dry (isoprene - 2.68 ± 0.9 ppbv, total monoterpenes - 0.67 ± 0.3 ppbv; total sesquiterpenes - 0.09 ± 0.07 ppbv) than the wet season (isoprene - 1.66 ± 0.9 ppbv, total monoterpenes - 0.47 ± 0.2 ppbv; total sesquiterpenes - 0.03 ± 0.02 ppbv) for all compounds. Ambient air temperature and photosynthetically active radiation (PAR) behaved similarly. Daytime isoprene and total monoterpene mixing ratios were highest within the canopy, rather than near the ground or above the canopy. By comparison, daytime total sesquiterpene mixing ratios were highest near the ground. Daytime fluxes varied significantly between seasons for all compounds. The maximums for isoprene (2.53 ± 0.5 μmol mg-2 hg-1) and total monoterpenes (1.77 ± 0.05μmol mg-2 hg-1) were observed in the late dry season, whereas the maximum for total sesquiterpenes was found during the dry-to-wet transition season (0.77 ± 0.1μmol mg-2 hg-1). These flux estimates suggest that the canopy is the main source of isoprenoids emitted into the atmosphere for all seasons. However, uncertainties in turbulence parameterization near the ground could affect estimates of fluxes that come from the ground. Leaf phenology seemed to be an important driver of seasonal variation of isoprenoid emissions. Although remote sensing observations of changes in leaf area index were used to estimate leaf phenology, MEGAN 2.1 did not fully capture the behavior of seasonal emissions observed in this study. This could be a result of very local effects on the observed emissions, but also suggest that other parameters need to be better determined in biogenic volatile organic compound (BVOC) models. Our results support established findings that seasonality of isoprenoids are driven by seasonal changes in light, temperature and leaf phenology. However, they suggest that leaf phenology and its role on isoprenoid production and emission from tropical plant species needs to be better understood in order to develop mechanistic explanations for seasonal variation in emissions. This also may reduce the uncertainties of model estimates associated with the responses to environmental factors. Therefore, this study strongly encourages long-term measurements of isoprenoid emissions, environmental factors and leaf phenology from leaf to ecosystem scale, with the purpose of improving BVOC model approaches that can characterize seasonality of isoprenoid emissions from tropical rainforests.

In humid tropical regions, irregular illumination and cloud shadows can complicate near-surface optical remote sensing. This could lead to costly and repetitive surveys to maintain geographical and spectral consistency. This could have a significant impact on the regular monitoring of forest ecosystems. A novel correction method using deep learning is presented here to address the issue in high-resolution canopy images. Our method involves training a deep learning model on one or a few well-illuminated/homogeneous reference images augmented with artificially generated cloud shadows. This training enables the model to predict illumination and cloud shadow patterns in any image and ultimately mitigate these effects. Using images captured by multispectral and RGB cameras, we evaluated the method across multiple sensors and conditions. These included nadir-view images from two sensors mounted on a drone and tower-mounted RGB Phenocams. The technique effectively corrects uneven illumination in near-infrared and true-color RGB images, including non-forested areas. This improvement was also evident in more consistent normalized difference vegetation index (NDVI) patterns in areas affected by uneven illumination. By comparing corrected RGB images to the original in a binary classification task, we evaluated the method's accuracy and Kappa values. Our goal was to detect non-photosynthetic vegetation (NPV) in a mosaic. The overall accuracy and Kappa were both significantly improved in corrected images, with a 2.5% and 1.1% increase, respectively. Moreover, the method can be generalized across sensors and conditions. Further work should focus on refining the technique and exploring its applicability to satellite imagery and beyond. [ABSTRACT FROM AUTHOR]