Secondary organic aerosol and organic nitrogen yields from the nitrate radical (NO&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;) oxidation of alpha-pinene from various RO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; fates

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Abstract

Abstract. The reaction of α-pinene with NO3 is an important sink of both α-pinene and NO3 at night in regions with mixed biogenic and anthropogenic emissions; however, there is debate on its importance for secondary organic aerosol (SOA) and reactive nitrogen budgets in the atmosphere. Previous experimental studies have generally observed low or zero SOA formation, often due to excessive [NO3] conditions. Here, we characterize the SOA and organic nitrogen formation from α-pinene + NO3 as a function of nitrooxy peroxy (nRO2) radical fates with HO2, NO, NO3, and RO2 in an atmospheric chamber. We show that SOA yields are not small when the nRO2 fate distribution in the chamber mimics that in the atmosphere, and the formation of pinene nitrooxy hydroperoxide (PNP) and related organonitrates in the ambient atmosphere can be reproduced. Nearly all SOA from α-pinene + NO3 chemistry derives from the nRO2+ RO2 pathway, which alone has an SOA mass yield of 56 (±7) %. Molecular composition analysis shows that particulate nitrates are a large (60 %–70 %) portion of the SOA and that dimer formation is the primary mechanism of SOA production from α-pinene + NO3 under simulated nighttime conditions. Synergistic dimerization between nRO2 and RO2 derived from ozonolysis and OH oxidation also contribute to SOA formation and should be considered in models. We report a 58 (±20) % molar yield of PNP from the nRO2+ HO2 pathway. Applying these laboratory constraints to model simulations of summertime conditions observed in the southeast United States (where 80 % of α-pinene is lost via NO3 oxidation, leading to 20 % nRO2+ RO2 and 45 % nRO2+ HO2), we estimate yields of 11 % SOA and 7 % particulate nitrate by mass and 26 % PNP by mole from α-pinene + NO3 in the ambient atmosphere. These results suggest that α-pinene + NO3 significantly contributes to the SOA budget and likely constitutes a major removal pathway of reactive nitrogen from the nighttime boundary layer in mixed biogenic–anthropogenic areas.

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Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry

Tobias Kind, Oliver Fiehn (2007)

Background Structure elucidation of unknown small molecules by mass spectrometry is a challenge despite advances in instrumentation. The first crucial step is to obtain correct elemental compositions. In order to automatically constrain the thousands of possible candidate structures, rules need to be developed to select the most likely and chemically correct molecular formulas. Results An algorithm for filtering molecular formulas is derived from seven heuristic rules: (1) restrictions for the number of elements, (2) LEWIS and SENIOR chemical rules, (3) isotopic patterns, (4) hydrogen/carbon ratios, (5) element ratio of nitrogen, oxygen, phosphor, and sulphur versus carbon, (6) element ratio probabilities and (7) presence of trimethylsilylated compounds. Formulas are ranked according to their isotopic patterns and subsequently constrained by presence in public chemical databases. The seven rules were developed on 68,237 existing molecular formulas and were validated in four experiments. First, 432,968 formulas covering five million PubChem database entries were checked for consistency. Only 0.6% of these compounds did not pass all rules. Next, the rules were shown to effectively reducing the complement all eight billion theoretically possible C, H, N, S, O, P-formulas up to 2000 Da to only 623 million most probable elemental compositions. Thirdly 6,000 pharmaceutical, toxic and natural compounds were selected from DrugBank, TSCA and DNP databases. The correct formulas were retrieved as top hit at 80–99% probability when assuming data acquisition with complete resolution of unique compounds and 5% absolute isotope ratio deviation and 3 ppm mass accuracy. Last, some exemplary compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and by gas chromatography-time of flight mass spectrometry. In each case, the correct formula was ranked as top hit when combining the seven rules with database queries. Conclusion The seven rules enable an automatic exclusion of molecular formulas which are either wrong or which contain unlikely high or low number of elements. The correct molecular formula is assigned with a probability of 98% if the formula exists in a compound database. For truly novel compounds that are not present in databases, the correct formula is found in the first three hits with a probability of 65–81%. Corresponding software and supplemental data are available for downloads from the authors' website.