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The exchange of trace gases between the biosphere and the atmosphere is an important process that controls both chemical and physical properties of the atmosphere with implications for air quality and climate change. The terrestrial biosphere is a major source of reactive biogenic volatile organic compounds (BVOCs) that govern atmospheric concentrations of the hydroxy radical (OH) and ozone (O3) and control the formation and growth of secondary organic aerosol (SOA). Common simulations of BVOC surface–atmosphere exchange in chemical transport models use parameterizations derived from the growing season and do not consider potential changes in emissions during seasonal transitions. Here, we use observations of BVOCs over a mixed temperate forest in northern Wisconsin during broadleaf senescence to better understand the effects of the seasonal changes in canopy conditions (e.g., temperature, sunlight, leaf area, and leaf stage) on net BVOC exchange. The BVOCs investigated here include the terpenoids isoprene (C5H8), monoterpenes (MTs; C10H16), a monoterpene oxide (C10H16O), and sesquiterpenes (SQTs; C15H24), as well as a subset of other monoterpene oxides and dimethyl sulfide (DMS). During this period, MTs were primarily composed of α-pinene, β-pinene, and camphene, with α-pinene and camphene dominant during the first half of September and β-pinene thereafter. We observed enhanced MT and monoterpene oxide emissions following the onset of leaf senescence and suggest that senescence has the potential to be a significant control on late-season MT emissions in this ecosystem. We show that common parameterizations of BVOC emissions cannot reproduce the fluxes of MT, C10H16O, and SQT during the onset and continuation of senescence but can correctly simulate isoprene flux. We also describe the impact of the MT emission enhancement on the potential to form highly oxygenated organic molecules (HOMs). The calculated production rates of HOMs and H2SO4, constrained by terpene and DMS concentrations, suggest that biogenic aerosol formation and growth in this region should be dominated by secondary organics rather than sulfate. Further, we show that models using parameterized MT emissions likely underestimate HOM production, and thus aerosol growth and formation, during early autumn in this region. Further measurements of forest–atmosphere BVOC exchange during seasonal transitions as well as measurements of DMS in temperate regions are needed to effectively predict the effects of canopy changes on reactive carbon cycling and aerosol production.

Chemical ionization mass spectrometry (CIMS) using iodide as a reagent ion has been widely used to classify organic compounds in the atmosphere by their elemental formula. Unfortunately, calibration of these instruments is challenging due to a lack of commercially available standards for many compounds, which has led to the development of methods for estimating CIMS sensitivity. By coupling a thermal desorption aerosol gas chromatograph (TAG) simultaneously to a flame ionization detector (FID) and an iodide CIMS, we use the individual particle-phase analytes, quantified by the FID, to examine the sensitivity of the CIMS and its variability between isomers of the same elemental formula. Iodide CIMS sensitivities of isomers within a formula are found to generally vary by 1 order of magnitude with a maximum deviation of 2 orders of magnitude. Furthermore, we compare directly measured sensitivity to a method of estimating sensitivity based on declustering voltage (i.e., "voltage scanning"). This approach is found to carry high uncertainties for individual analytes (0.5 to 1 order of magnitude) but represents a central tendency that can be used to estimate the sum of analytes with reasonable error (similar to 30% differences between predicted and measured moles). Finally, gas chromatography (GC) retention time, which is associated with vapor pressure and chemical functionality of an analyte, is found to qualitatively correlate with iodide CIMS sensitivity, but the relationship is not close enough to be quantitatively useful and could be explored further in the future as a potential calibration approach. Alfred P. Sloan FoundationAlfred P. Sloan Foundation [P-2018-11129] Published version This research has been supported by the Alfred P. Sloan Foundation (grant no. P-2018-11129).

Environmental samples typically contain hundreds or thousands of unique organic compounds, and even minor components may provide valuable insight into their sources and transformations. To understand atmospheric processes, individual components are frequently identified and quantified using gas chromatography-mass spectrometry. However, due to the complexity and frequently variable nature of such data, data reduction is a significant bottleneck in analysis. Consequently, only a subset of known analytes is often reported for a dataset, and large amounts of potentially useful data are discarded. We present an automated approach of cataloging and potentially identifying all analytes in a large chromatographic dataset and demonstrate the utility of our approach in an analysis of ambient aerosols. We use a coupled factor analysis-decision tree approach to deconvolute peaks and comprehensively catalog nearly all analytes in a dataset. Positive matrix factorization (PMF) of small subsections of multiple chromatograms is applied to extract factors that represent chromatographic profiles and mass spectra of potential analytes, in which peaks are detected. A decision tree based on peak parameters (e.g., location, width, and height), relative ratios of those parameters, peak shape, noise, retention time, and mass spectrum is applied to discard erroneous peaks and combine peaks determined to represent the same analyte. With our approach, all analytes within the small section of the chromatogram are cataloged, and the process is repeated for overlapping sections across the chromatogram, generating a complete list of the retention times and estimated mass spectra of all peaks in a dataset. We validate this approach using samples of known compounds and demonstrate the separation of poorly resolved peaks with similar mass spectra and the resolution of peaks that appear in only a fraction of chromatograms. As a case study, this method is applied to a complex real-world dataset of the composition of atmospheric particles, in which more than 1100 unique chromatographic peaks are resolved, and the corresponding peak information along with mass spectra are cataloged. National Oceanic and Atmospheric Administration Small Business Innovative Research Program [WC133R18CN0064, NA21OAR0210294] Published version This work was supported by the National Oceanic and Atmospheric Administration Small Business Innovative Research Program (WC133R18CN0064 and NA21OAR0210294). We thank Chenyang Bi for assistance with generating laboratory data and Allen Goldstein for sharing ambient data.