40 research outputs found
pH and Solvent Influence on p-Aminobenzoic Acid
Through X-ray absorption and emission spectroscopies, the chemical, electronic
and structural properties of organic species in solution can be observed.
Near-edge X-ray absorption fine structure (NEXAFS) and resonant inelastic
X-ray scattering (RIXS) measurements at the nitrogen K-edge of para-
aminobenzoic acid reveal both pH- and solvent-dependent variations in the
ionisation potential (IP), 1s→π* resonances and HOMO–LUMO gap. These changes
unequivocally identify the chemical species (neutral, cationic or anionic)
present in solution. It is shown how this incisive chemical state sensitivity
is further enhanced by the possibility of quantitative bond length
determination, based on the analysis of chemical shifts in IPs and σ* shape
resonances in the NEXAFS spectra. This provides experimental access to
detecting even minor variations in the molecular structure of solutes in
solution, thereby providing an avenue to examining computational predictions
of solute properties and solute–solvent interactions
Farmer Perceptions of Land Cover Classification of UAS Imagery of Coffee Agroecosystems in Puerto Rico
Highly diverse agroecosystems are increasingly of interest as the realization of farms’ invaluable ecosystem services grows. Simultaneously there has been an increased use of uncrewed aerial systems (UAS) in remote sensing as drones offer a finer spatial resolution and faster revisit rate than traditional satellites. With the combined utility of UAS and the attention on agroecosystems, there exists an opportunity to assess UAS practicality in highly biodiverse settings. In this study, we utilized UAS to collect fine-resolution 10-band multispectral imagery of coffee agroecosystems in Puerto Rico. We created land cover maps through a pixel-based supervised classification of each farm and assembled accuracy assessments for each classification. To bolster our understanding of the classifications, we interviewed farmers to understand their thoughts on how these maps may be best used to support their land management. The average overall accuracy (53.9%), though relatively low, was expected for such a diverse landscape with fine-resolution data. After sharing imagery and land cover classifications with farmers, we found that while the prints were often a point of pride or curiosity for farmers, integrating the maps into farm management was perceived as impractical. These findings highlight that while remote sensing of diverse agroecosystems may provide a detailed way of estimating land cover classes and ecosystem services for researchers and government agencies for example these maps may be of limited use to land managers without additional interpretation
Mapping Regional Inundation with Spaceborne L-Band SAR
Shortly after the launch of ALOS PALSAR L-band SAR by the Japan Space Exploration Agency (JAXA), a program to develop an Earth Science Data Record (ESDR) for inundated wetlands was funded by NASA. Using established methodologies, extensive multi-temporal L-band ALOS ScanSAR data acquired bi-monthly by the PALSAR instrument onboard ALOS were used to classify the inundation state for South America for delivery as a component of this Inundated Wetlands ESDR (IW-ESDR) and in collaboration with JAXA’s ALOS Kyoto and Carbon Initiative science programme. We describe these methodologies and the final classification of the inundation state, then compared this with results derived from dual-season data acquired by the JERS-1 L-band SAR mission in 1995 and 1996, as well as with estimates of surface water extent measured globally every 10 days by coarser resolution sensors. Good correspondence was found when comparing open water extent classified from multi-temporal ALOS ScanSAR data with surface water fraction identified from coarse resolution sensors, except in those regions where there may be differences in sensitivity to widespread and shallow seasonal flooding event, or in areas that could be excluded through use of a continental-scale inundatable mask. It was found that the ALOS ScanSAR classification of inundated vegetation was relatively insensitive to inundated herbaceous vegetation. Inundation dynamics were examined using the multi-temporal ALOS ScanSAR acquisitions over the Pacaya-Samiria and surrounding areas in the Peruvian Amazon
Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data
The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at 25-km resolution. The approach employs a land cover-supported, atmospherically-corrected dynamic mixture model applied to 20+ years (1992–2013) of combined, daily, passive/active microwave remote sensing data. The resulting product, known as Surface WAter Microwave Product Series (SWAMPS), shows strong microwave sensitivity to sub-grid scale open water and inundated wetlands comprising open plant canopies. SWAMPS’ FW compares favorably (R2 = 91%–94%) with higher-resolution, global-scale maps of open water from MODIS and SRTM-MOD44W. Correspondence of SWAMPS with open water and wetland products from satellite SAR in Alaska and the Amazon deteriorates when exposed wetlands or inundated forests captured by the SAR products were added to the open water fraction reflecting SWAMPS’ inability to detect water underneath the soil surface or beneath closed forest canopies. Except for a brief period of drying during the first 4 years of observation, the inundation extent for the global domain excluding the coast was largely stable. Regionally, inundation in North America is advancing while inundation is on the retreat in Tropical Africa and North Eurasia. SWAMPS provides a consistent and long-term global record of daily FW dynamics, with documented accuracies suitable for hydrologic assessment and global change-related investigations
Intermolecular Bonding of Hemin in Solution and in Solid State Probed by N K-edge X-ray Spectroscopies
X-ray absorption/emission spectroscopy (XAS/XES) at the N K-edge of iron protoporphyrin IX chloride (FePPIX-Cl, or hemin) has been carried out for dissolved monomers in DMSO, dimers in water and for the solid state. This sequence of samples permits identification of characteristic spectral features associated with the hemin intermolecular bonding. These characteristic features are further analyzed and understood at the molecular orbital (MO) level based on the DFT calculations
LipidXplorer: A Software for Consensual Cross-Platform Lipidomics
LipidXplorer is the open source software that supports the quantitative characterization of complex lipidomes by interpreting large datasets of shotgun mass spectra. LipidXplorer processes spectra acquired on any type of tandem mass spectrometers; it identifies and quantifies molecular species of any ionizable lipid class by considering any known or assumed molecular fragmentation pathway independently of any resource of reference mass spectra. It also supports any shotgun profiling routine, from high throughput top-down screening for molecular diagnostic and biomarker discovery to the targeted absolute quantification of low abundant lipid species. Full documentation on installation and operation of LipidXplorer, including tutorial, collection of spectra interpretation scripts, FAQ and user forum are available through the wiki site at: https://wiki.mpi-cbg.de/wiki/lipidx/index.php/Main_Page
Global wetland contribution to 2000-2012 atmospheric methane growth rate dynamics
Increasing atmospheric methane (CH4) concentrations have contributed to approximately 20% of anthropogenic climate change. Despite the importance of CH4 as a greenhouse gas, its atmospheric growth rate and dynamics over the past two decades, which include a stabilization period (1999–2006), followed by renewed growth starting in 2007, remain poorly understood. We provide an updated estimate of CH4 emissions from wetlands, the largest natural global CH4 source, for 2000–2012 using an ensemble of biogeochemical models constrained with remote sensing surface inundation and inventory-based wetland area data. Between 2000–2012, boreal wetland CH4 emissions increased by 1.2 Tg yr−1 (−0.2–3.5 Tg yr−1), tropical emissions decreased by 0.9 Tg yr−1 (−3.2−1.1 Tg yr−1), yet globally, emissions remained unchanged at 184 ± 22 Tg yr−1. Changing air temperature was responsible for increasing high-latitude emissions whereas declines in low-latitude wetland area decreased tropical emissions; both dynamics are consistent with features of predicted centennial-scale climate change impacts on wetland CH4 emissions. Despite uncertainties in wetland area mapping, our study shows that global wetland CH4 emissions have not contributed significantly to the period of renewed atmospheric CH4 growth, and is consistent with findings from studies that indicate some combination of increasing fossil fuel and agriculture-related CH4 emissions, and a decrease in the atmospheric oxidative sink
Variability and quasi-decadal changes in the methane budget overthe period 2000–2012
Following the recent Global Carbon Project (GCP)
synthesis of the decadal methane (CH4/ budget over 2000–
2012 (Saunois et al., 2016), we analyse here the same dataset
with a focus on quasi-decadal and inter-annual variability in
CH4 emissions. The GCP dataset integrates results from topdown
studies (exploiting atmospheric observations within an
atmospheric inverse-modelling framework) and bottom-up
models (including process-based models for estimating land
surface emissions and atmospheric chemistry), inventories of
anthropogenic emissions, and data-driven approaches.The annual global methane emissions from top-down studies,
which by construction match the observed methane
growth rate within their uncertainties, all show an increase in
total methane emissions over the period 2000–2012, but this
increase is not linear over the 13 years. Despite differences
between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total
methane emissions over the period 2000–2006, during
the plateau of atmospheric methane mole fractions, and also
over the period 2008–2012, during the renewed atmospheric
methane increase. However, the top-down ensemble mean
produces an emission shift between 2006 and 2008, leading
to 22 [16–32] Tg CH4 yr1 higher methane emissions
over the period 2008–2012 compared to 2002–2006. This
emission increase mostly originated from the tropics, with
a smaller contribution from mid-latitudes and no significant
change from boreal regions.
The regional contributions remain uncertain in top-down
studies. Tropical South America and South and East Asia
seem to contribute the most to the emission increase in the
tropics. However, these two regions have only limited atmospheric
measurements and remain therefore poorly constrained.
The sectorial partitioning of this emission increase between
the periods 2002–2006 and 2008–2012 differs from
one atmospheric inversion study to another. However, all topdown
studies suggest smaller changes in fossil fuel emissions
(from oil, gas, and coal industries) compared to the
mean of the bottom-up inventories included in this study.
This difference is partly driven by a smaller emission change
in China from the top-down studies compared to the estimate
in the Emission Database for Global Atmospheric Research
(EDGARv4.2) inventory, which should be revised to smaller
values in a near future. We apply isotopic signatures to the
emission changes estimated for individual studies based on
five emission sectors and find that for six individual top-down
studies (out of eight) the average isotopic signature of the
emission changes is not consistent with the observed change
in atmospheric 13CH4. However, the partitioning in emission
change derived from the ensemble mean is consistent with
this isotopic constraint. At the global scale, the top-down ensemble
mean suggests that the dominant contribution to the
resumed atmospheric CH4 growth after 2006 comes from microbial
sources (more from agriculture and waste sectors than
from natural wetlands), with an uncertain but smaller contribution
from fossil CH4 emissions. In addition, a decrease in
biomass burning emissions (in agreement with the biomass
burning emission databases) makes the balance of sources
consistent with atmospheric 13CH4 observations.
In most of the top-down studies included here, OH concentrations
are considered constant over the years (seasonal variations
but without any inter-annual variability). As a result,
the methane loss (in particular through OH oxidation) varies
mainly through the change in methane concentrations and not
its oxidants. For these reasons, changes in the methane loss
could not be properly investigated in this study, although it
may play a significant role in the recent atmospheric methane
changes as briefly discussed at the end of the paper.Published11135–111616A. Geochimica per l'ambienteJCR Journa
Data from: Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling
Remote sensing techniques offer an opportunity to improve biodiversity modeling and prediction worldwide. Yet, to date, the weather-station based WorldClim dataset has been the primary source of temperature and precipitation information used in correlative species distribution models. WorldClim consists of grids interpolated from in situ station data recorded primarily from 1960 to 1990. Those datasets suffer from uneven geographic coverage, with many areas of Earth poorly represented. Here, we compare two remote sensing data sources for the purposes of biodiversity prediction: MERRA climate reanalysis data and AMSR-E, a pure remote sensing data source. We use these data to generate novel temperature-based bioclimatic information and to model the distributions of 20 species of vertebrates endemic to four regions of South America: Amazonia, the Atlantic Forest, the Cerrado, and Patagonia. We compare the bioclimatic datasets derived from MERRA and AMSR-E information with in situ station data, and contrast species distribution models based on these two products to models built with WorldClim. Surface temperature estimates provided by MERRA and AMSR-E showed warm temperature biases relative to the in situ data fields, but the reliability of these datasets varied in geographic space. Species distribution models derived from the MERRA data performed equally well (in Cerrado, Amazonia, and Patagonia) or better (Atlantic Forest) than models built with the WorldClim data. In contrast, the performance of models constructed with the AMSR-E data was similar to (Amazonia, Atlantic Forest, Cerrado) or worse than (Patagonia) that of models built with WorldClim data. Whereas this initial comparison assessed only temperature fields, efforts to estimate precipitation from remote sensing information hold great promise; furthermore, other environmental datasets with higher spatial and temporal fidelity may improve upon these results