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Seagrass meadows are autotrophic ecosystems acting as carbon sinks, but they have also been shown to be sources of carbon dioxide (CO2) and methane (CH4). Seagrasses can be negatively affected by increasing seawater temperatures, but the effects of warming on CO2 and CH4 fluxes in seagrass meadows have not yet been reported. Here, we examine the effect of two disturbances on air–seawater fluxes of CO2 and CH4 in Red Sea Halophila stipulacea communities compared to adjacent unvegetated sediments using cavity ring-down spectroscopy. We first characterized CO2 and CH4 fluxes in vegetated and adjacent unvegetated sediments, and then experimentally examined their response, along with that of the carbon (C) isotopic signature of CO2 and CH4, to gradual warming from 25 ∘C (winter seawater temperature) to 37 ∘C, 2 ∘C above current maximum temperature. In addition, we assessed the response to prolonged darkness, thereby providing insights into the possible role of suppressing plant photosynthesis in supporting CO2 and CH4 fluxes. We detected 6-fold-higher CO2 fluxes in vegetated compared to bare sediments, as well as 10- to 100-fold-higher CH4 fluxes. Warming led to an increase in net CO2 and CH4 fluxes, reaching average fluxes of 10 422.18 ± 2570.12 µmol CO2 m−2 d−1 and 88.11±15.19 µmol CH4 m−2 d−1, while CO2 and CH4 fluxes decreased over time in sediments maintained at 25 ∘C. Prolonged darkness led to an increase in CO2 fluxes but a decrease in CH4 fluxes in vegetated sediments. These results add to previous research identifying Red Sea seagrass meadows as a significant source of CH4, while also indicating that sublethal warming may lead to increased emissions of greenhouse gases from seagrass meadows, providing a feedback mechanism that may contribute to further enhancing global warming.

The Fourier transform infrared (FTIR) spectra of fine particulate matter (PM2.5) contain many important absorption bands relevant for characterizing organic matter (OM) and obtaining organic matter to organic carbon (OM∕OC) ratios. However, extracting this information quantitatively – accounting for overlapping absorption bands and relating absorption to molar abundance – and furthermore relating abundances of functional groups to that of carbon atoms poses several challenges. In this work, we define a set of parameters that model these relationships and apply a probabilistic framework to identify values consistent with collocated field measurements of thermal–optical reflectance organic carbon (TOR OC). Parameter values are characterized for various sample types identified by cluster analysis of sample FTIR spectra, which are available for 17 sites in the Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network (7 sites in 2011 and 10 additional sites in 2013). The cluster analysis appears to separate samples according to predominant influence by dust, residential wood burning, wildfire, urban sources, and biogenic aerosols. Functional groups calibrations of aliphatic CH, alcohol COH, carboxylic acid COOH, carboxylate COO, and amine NH2 combined together reproduce TOR OC concentrations with reasonable agreement (r=0.96 for 2474 samples) and provide OM∕OC values generally consistent with our current best estimate of ambient OC. The mean OM∕OC ratios corresponding to sample types determined from cluster analysis range between 1.4 and 2.0, though ratios for individual samples exhibit a larger range. Trends in OM∕OC for sites aggregated by region or year are compared with another regression approach for estimating OM∕OC ratios from a mass closure equation of the major chemical species contributing to PM fine mass. Differences in OM∕OC estimates are observed according to estimation method and are explained through the sample types determined from spectral profiles of the PM.

Comprehensive techniques to describe the organic composition of atmospheric aerosol are needed to elucidate pollution sources, gain insights into atmospheric chemistry, and evaluate changes in air quality. Fourier transform infrared absorption (FT-IR) spectrometry can be used to characterize atmospheric organic matter (OM) and its composition via functional groups of aerosol filter samples in air monitoring networks and research campaigns. We have built FT-IR spectrometry functional group calibration models that improve upon previous work, as demonstrated by the comparison of current model results with those of previous models and other OM analysis methods. Laboratory standards that simulated the breadth of the absorbing functional groups in atmospheric OM were made: particles of relevant chemicals were first generated, collected, and analyzed. Challenges of collecting atmospherically relevant particles and spectra were addressed by including interferences of particle water and other inorganic aerosol constituents and exploring the spectral effects of intermolecular interactions. Calibration models of functional groups were then constructed using partial least-squares (PLS) regression and the collected laboratory standard data. These models were used to quantify concentrations of five organic functional groups and OM in 8 years of ambient aerosol samples from the southeastern aerosol research and characterization (SEARCH) network. The results agreed with values estimated using other methods, including thermal optical reflectance (TOR) organic carbon (OC; R2=0.74) and OM calculated as a difference between total aerosol mass and inorganic species concentrations (R2=0.82). Comparisons with previous calibration models of the same type demonstrate that this new, more complete suite of chemicals has improved our ability to estimate oxygenated functional group and overall OM concentrations. Calculated characteristic and elemental ratios including OM∕OC, O∕C, and H∕C agree with those from previous work in the southeastern US, substantiating the aerosol composition described by FT-IR calibration. The median OM∕OC ratio over all sites and years was 2.1±0.2. Further results discussing temporal and spatial trends of functional group composition within the SEARCH network will be published in a forthcoming article.

Atmospheric particulate matter (PM) is a complex mixture of many different substances and requires a suite of instruments for chemical characterization. Fourier transform infrared (FT-IR) spectroscopy is a technique that can provide quantification of multiple species provided that accurate calibration models can be constructed to interpret the acquired spectra. In this capacity, FT-IR spectroscopy has enjoyed a long history in monitoring gas-phase constituents in the atmosphere and in stack emissions. However, application to PM poses a different set of challenges as the condensed-phase spectrum has broad, overlapping absorption peaks and contributions of scattering to the mid-infrared spectrum. Past approaches have used laboratory standards to build calibration models for prediction of inorganic substances or organic functional groups and predict their concentration in atmospheric PM mixtures by extrapolation. In this work, we review recent studies pursuing an alternate strategy, which is to build statistical calibration models for mid-IR spectra of PM using collocated ambient measurements. Focusing on calibrations with organic carbon (OC) and elemental carbon (EC) reported from thermal–optical reflectance (TOR), this synthesis serves to consolidate our knowledge for extending FT-IR spectroscopy to provide TOR-equivalent OC and EC measurements to new PM samples when TOR measurements are not available. We summarize methods for model specification, calibration sample selection, and model evaluation for these substances at several sites in two US national monitoring networks: seven sites in the Interagency Monitoring of Protected Visual Environments (IMPROVE) network for the year 2011 and 10 sites in the Chemical Speciation Network (CSN) for the year 2013. We then describe application of the model in an operational context for the IMPROVE network for samples collected in 2013 at six of the same sites as in 2011 and 11 additional sites. In addition to extending the evaluation to samples from a different year and different sites, we describe strategies for error anticipation due to precision and biases from the calibration model to assess model applicability for new spectra a priori. We conclude with a discussion regarding past work and future strategies for recalibration. In addition to targeting numerical accuracy, we encourage model interpretation to facilitate understanding of the underlying structural composition related to operationally defined quantities of TOR OC and EC from the vibrational modes in mid-IR deemed most informative for calibration. The paper is structured such that the life cycle of a statistical calibration model for FT-IR spectroscopy can be envisioned for any substance with IR-active vibrational modes, and more generally for instruments requiring ambient calibrations.

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