Effects of gas–wall partitioning in Teflon tubing and instrumentation on time-resolved measurements of gas-phase organic compounds

Recent studies have demonstrated that organic compounds can partition from the gas phase to the walls in Teflon environmental chambers and that the process can be modeled as absorptive partitioning. Here these studies were extended to investigate gas–wall partitioning of organic compounds in Teflon tubing and inside a proton-transfer-reaction mass spectrometer (PTR-MS) used to monitor compound concentrations. Rapid partitioning of C8–C14 2-ketones and C11–C16 1-alkenes was observed for compounds with saturation concentrations (c∗) in the range of 3 × 104 to 1 × 107 µg m−3, causing delays in instrument response to step-function changes in the concentration of compounds being measured. These delays vary proportionally with tubing length and diameter and inversely with flow rate and c∗. The gas–wall partitioning process that occurs in tubing is similar to what occurs in a gas chromatography column, and the measured delay times (analogous to retention times) were accurately described using a linear chromatography model where the walls were treated as an equivalent absorbing mass that is consistent with values determined for Teflon environmental chambers. The effect of PTR-MS surfaces on delay times was also quantified and incorporated into the model. The model predicts delays of an hour or more for semivolatile compounds measured under commonly employed conditions. These results and the model can enable better quantitative design of sampling systems, in particular when fast response is needed, such as for rapid transients, aircraft, or eddy covariance measurements. They may also allow estimation of c∗ values for unidentified organic compounds detected by mass spectrometry and could be employed to introduce differences in time series of compounds for use with factor analysis methods. Best practices are suggested for sampling organic compounds through Teflon tubing

Global Budget of Methanol: Constraints from Atmospheric Observations

We use a global three-dimensional model simulation of atmospheric methanol to examine the consistency between observed atmospheric concentrations and current understanding of sources and sinks. Global sources in the model include 128 Tg yr−1 from plant growth, 38 Tg yr−1 from atmospheric reactions of CH3O2 with itself and other organic peroxy radicals, 23 Tg yr−1 from plant decay, 13 Tg yr−1 from biomass burning and biofuels, and 4 Tg yr−1 from vehicles and industry. The plant growth source is a factor of 3 higher for young than from mature leaves. The atmospheric lifetime of methanol in the model is 7 days; gas-phase oxidation by OH accounts for 63% of the global sink, dry deposition to land 26%, wet deposition 6%, uptake by the ocean 5%, and aqueous-phase oxidation in clouds less than 1%. The resulting simulation of atmospheric concentrations is generally unbiased in the Northern Hemisphere and reproduces the observed correlations of methanol with acetone, HCN, and CO in Asian outflow. Accounting for decreasing emission from leaves as they age is necessary to reproduce the observed seasonal variation of methanol concentrations at northern midlatitudes. The main model discrepancy is over the South Pacific, where simulated concentrations are a factor of 2 too low. Atmospheric production from the CH3O2 self-reaction is the dominant model source in this region. A factor of 2 increase in this source (to 50–100 Tg yr−1) would largely correct the discrepancy and appears consistent with independent constraints on CH3O2 concentrations. Our resulting best estimate of the global source of methanol is 240 Tg yr−1. More observations of methanol concentrations and fluxes are needed over tropical continents. Better knowledge is needed of CH3O2 concentrations in the remote troposphere and of the underlying organic chemistry.Earth and Planetary Science

Secondary organic aerosol production from local emissions dominates the organic aerosol budget over Seoul, South Korea, during KORUS-AQ

Organic aerosol (OA) is an important fraction of submicron aerosols. However, it is challenging to predict and attribute the specific organic compounds and sources that lead to observed OA loadings, largely due to contributions from secondary production. This is especially true for megacities surrounded by numerous regional sources that create an OA background. Here, we utilize in situ gas and aerosol observations collected on board the NASA DC-8 during the NASA&ndash;NIER KORUS-AQ (Korea&ndash;United States Air Quality) campaign to investigate the sources and hydrocarbon precursors that led to the secondary OA (SOA) production observed over Seoul. First, we investigate the contribution of transported OA to total loadings observed over Seoul by using observations over the Yellow Sea coupled to FLEXPART Lagrangian simulations. During KORUS-AQ, the average OA loading advected into Seoul was &sim;1&ndash;3&thinsp;&micro;g&thinsp;sm&minus;3. Second, taking this background into account, the dilution-corrected SOA concentration observed over Seoul was &sim;140&thinsp;&micro;gsm-3ppmv-1 at 0.5 equivalent photochemical days. This value is at the high end of what has been observed in other megacities around the world (20&ndash;70&thinsp;&micro;gsm-3ppmv-1 at 0.5 equivalent days). For the average OA concentration observed over Seoul (13&thinsp;&micro;g&thinsp;sm&minus;3), it is clear that production of SOA from locally emitted precursors is the major source in the region. The importance of local SOA production was supported by the following observations. (1) FLEXPART source contribution calculations indicate any hydrocarbons with a lifetime of less than 1 day, which are shown to dominate the observed SOA production, mainly originate from South Korea. (2) SOA correlated strongly with other secondary photochemical species, including short-lived species (formaldehyde, peroxy acetyl nitrate, sum of acyl peroxy nitrates, dihydroxytoluene, and nitrate aerosol). (3) Results from an airborne oxidation flow reactor (OFR), flown for the first time, show a factor of 4.5 increase in potential SOA concentrations over Seoul versus over the Yellow Sea, a region where background air masses that are advected into Seoul can be measured. (4) Box model simulations reproduce SOA observed over Seoul within 11&thinsp;% on average and suggest that short-lived hydrocarbons (i.e., xylenes, trimethylbenzenes, and semi-volatile and intermediate-volatility compounds) were the main SOA precursors over Seoul. Toluene alone contributes 9&thinsp;% of the modeled SOA over Seoul. Finally, along with these results, we use the metric &Delta;OA/&Delta;CO2 to examine the amount of OA produced per fuel consumed in a megacity, which shows less variability across the world than &Delta;OA∕&Delta;CO.</p

Volatile organic compounds composition of merged and aged forest fire plumes from Alaska and western Canada

The NOAA WP-3 aircraft intercepted aged forest fire plumes from Alaska and western Canada during several flights of the NEAQS-ITCT 2k4 mission in 2004. Measurements of acetonitrile (CH3CN) indicated that the air masses had been influenced by biomass burning. The locations of the plume intercepts were well described using emissions estimates and calculations with the transport model FLEXPART. The best description of the data was generally obtained when FLEXPART injected the forest fire emissions to high altitudes in the model. The observed plumes were generally drier than the surrounding air masses at the same altitude, suggesting that the fire plumes had been processed by clouds and that moisture had been removed by precipitation. Different degrees of photochemical processing of the plumes were determined from the measurements of aromatic VOCs. The removal of aromatic VOCs was slow considering the transport times estimated from the FLEXPART model. This suggests that the average OH levels were low during the transport, which may be explained by the low humidity and high concentrations of carbon monoxide and other pollutants. In contrast with previous work, no strong secondary production of acetone, methanol and acetic acid is inferred from the measurements. A clear case of removal of submicron particle volume and acetic acid due to precipitation scavenging was observed. Copyright 2006 by the American Geophysical Union

Laboratory measurements of trace gas emissions from biomass burning of fuel types from the southeastern and southwestern United States

Vegetation commonly managed by prescribed burning was collected from five southeastern and southwestern US military bases and burned under controlled conditions at the US Forest Service Fire Sciences Laboratory in Missoula, Montana. The smoke emissions were measured with a large suite of state-of-the-art instrumentation including an open-path Fourier transform infrared (OP-FTIR) spectrometer for measurement of gas-phase species. The OP-FTIR detected and quantified 19 gas-phase species in these fires: CO2, CO, CH4, C2H2, C2H4, C3H6, HCHO, HCOOH, CH3OH, CH3COOH, furan, H2O, NO, NO2, HONO, NH3, HCN, HCl, and SO2. Emission factors for these species are presented for each vegetation type burned. Gas-phase nitrous acid (HONO), an important OH precursor, was detected in the smoke from all fires. The HONO emission factors ranged from 0.15 to 0.60 g kg(-1) and were higher for the southeastern fuels. The fire-integrated molar emission ratios of HONO (relative to NOx) ranged from approximately 0.03 to 0.20, with higher values also observed for the southeastern fuels. The majority of non-methane organic compound (NMOC) emissions detected by OP-FTIR were oxygenated volatile organic compounds (OVOCs) with the total identified OVOC emissions constituting 61 +/- 12% of the total measured NMOC on a molar basis. These OVOC may undergo photolysis or further oxidation contributing to ozone formation. Elevated amounts of gas-phase HCl and SO2 were also detected during flaming combustion, with the amounts varying greatly depending on location and vegetation type. The fuels with the highest HCl emission factors were all located in the coastal regions, although HCl was also observed from fuels farther inland. Emission factors for HCl were generally higher for the southwestern fuels, particularly those found in the chaparral biome in the coastal regions of California

Primary emissions of glyoxal and methylglyoxal from laboratory measurements of open biomass burning

We report the emissions of glyoxal and methylglyoxal from the open burning of biomass during the NOAA-led 2016 FIREX intensive at the Fire Sciences Laboratory in Missoula, MT. Both compounds were measured using cavity-enhanced spectroscopy, which is both more sensitive and more selective than methods previously used to determine emissions of these two compounds. A total of 75 burns were conducted, using 33 different fuels in 8 different categories, providing a far more comprehensive dataset for emissions than was previously available. Measurements of methylglyoxal using our instrument suffer from spectral interferences from several other species, and the values reported here are likely underestimates, possibly by as much as 70 %. Methylglyoxal emissions were 2-3 times higher than glyoxal emissions on a molar basis, in contrast to previous studies that report methylglyoxal emissions lower than glyoxal emissions. Methylglyoxal emission ratios for all fuels averaged 3.6±2.4 ppbv methylglyoxal (ppmv CO) 1, while emission factors averaged 0.66±0.50 g methylglyoxal (kg fuel burned) 1. Primary emissions of glyoxal from biomass burning were much lower than previous laboratory measurements but consistent with recent measurements from aircraft. Glyoxal emission ratios for all fuels averaged 1.4±0.7 ppbv glyoxal (ppmv CO) 1, while emission factors averaged 0.20±0.12 g glyoxal (kg fuel burned) 1, values that are at least a factor of 4 lower than assumed in previous estimates of the global glyoxal budget. While there was significant variability in the glyoxal emission ratios and factors between the different fuel groups, glyoxal and formaldehyde were highly correlated during the course of any given fire, and the ratio of glyoxal to formaldehyde, RGF, was consistent across many different fuel types, with an average value of 0.068±0.018. While RGF values for fresh emissions were consistent across many fuel types, further work is required to determine how this value changes as the emissions age

Drivers of cloud droplet number variability in the summertime in the southeastern United States

Here we analyze regional-scale data collected on board the NOAA WP-3D aircraft during the 2013 Southeast Nexus (SENEX) campaign to study the aerosol&ndash;cloud droplet link and quantify the sensitivity of droplet number to aerosol number, chemical composition, and vertical velocity. For this, the observed aerosol size distributions, chemical composition, and vertical-velocity distribution are introduced into a state-of-the-art cloud droplet parameterization to show that cloud maximum supersaturations in the region range from 0.02&thinsp;% to 0.52&thinsp;%, with an average of 0.14&plusmn;0.05&thinsp;%. Based on these low values of supersaturation, the majority of activated droplets correspond to particles with a dry diameter of 90&thinsp;nm and above. An important finding is that the standard deviation of the vertical velocity (&sigma;w) exhibits considerable diurnal variability (ranging from 0.16&thinsp;m&thinsp;s&minus;1 during nighttime to over 1.2&thinsp;m&thinsp;s&minus;1 during day), and it tends to covary with total aerosol number (Na). This &sigma;w&ndash;Na covariance amplifies the predicted response in cloud droplet number (Nd) to Na increases by 3 to 5 times compared to expectations based on Na changes alone. This amplified response is important given that droplet formation is often velocity-limited and therefore should normally be insensitive to aerosol changes. We also find that Nd cannot exceed a characteristic concentration that depends solely on &sigma;w. Correct consideration of &sigma;w and its covariance with time and Na is important for fully understanding aerosol&ndash;cloud interactions and the magnitude of the aerosol indirect effect. Given that model assessments of aerosol&ndash;cloud&ndash;climate interactions do not routinely evaluate for overall turbulence or its covariance with other parameters, datasets and analyses such as the one presented here are of the highest priority to address unresolved sources of hydrometeor variability, bias, and the response of droplet number to aerosol perturbations. </div