Biogenic emission measurement and inventories determination of biogenic emissions in the eastern United States and Texas and comparison with biogenic emission inventories

Abstract

During the NOAA Southern Oxidant Study 1999 (SOS1999), Texas Air Quality Study 2000 (TexAQS2000), International Consortium for Atmospheric Research on Transport and Transformation (ICARTT2004), and Texas Air Quality Study 2006 (TexAQS2006) campaigns, airborne measurements of isoprene and monoterpenes were made in the eastern United States and in Texas, and the results are used to evaluate the biogenic emission inventories BEIS3.12, BEIS3.13, MEGAN2, and WM2001. Two methods are used for the evaluation. First, the emissions are directly estimated from the ambient isoprene and monoterpene measurements assuming a well‐mixed boundary layer and are compared with the emissions from the inventories extracted along the flight tracks. Second, BEIS3.12 is incorporated into the detailed transport model FLEXPART, which allows the isoprene and monoterpene mixing ratios to be calculated and compared to the measurements. The overall agreement for all inventories is within a factor of 2 and the two methods give consistent results. MEGAN2 is in most cases higher, and BEIS3.12 and BEIS3.13 lower than the emissions determined from the measurements. Regions with clear discrepancies are identified. For example, an isoprene hot spot to the northwest of Houston, Texas, was expected from BEIS3 but not observed in the measurements. Interannual differences in emissions of about a factor of 2 were observed in Texas between 2000 and 2006.

Differences between measured and reported volatile organic compound emissions from oil sands facilities in Alberta, Canada

“…The methodology uses a combination of box-like aircraft flight patterns enclosing each of the main surface mining facilities, comprehensive measurement methods at high time resolution, and the development and application of a computational top-down emission rate retrieval algorithm (TERRA) (25) for deriving facility emission rates from measurements of chemical concentration and meteorology. In contrast to previous aircraft VOC emission studies (26)(27)(28)(29), horizontal and vertical advection and diffusion, chemical reactions, and air mass density changes, all based on measurements, were included in the computation of mass balance of the VOCs from the aircraft measurements, thus reducing the uncertainties in the derived emission rates (25). Another advantage of this methodology is its ability to provide the emission rates of tracers used for the determination of emission rates for many VOCs from discrete measurements, such as canister sampling.…”

Potential Role of Stabilized Criegee Radicals in Sulfuric Acid Production in a High Biogenic VOC Environment

“…This is much lower than the reported values (∼2 × 10 7 molecules cm −3 ) for airborne observations over the boundary layer of Northeastern U.S. and the Ohio Valley region during NEAQS-2004. 40,41 As one of the major aims of the NEAQS-2004 campaign was to sample power plant plumes, it is 40 and Beijing, China 42 were reported as 1.6 × 10 7 and 5 × 10 6 molecules cm −3 , respectively, although the observed SO 2 levels were at similar levels of ∼5−10 ppb for both campaigns. This nonlinearity between H 2 SO 4 and its precursor, SO 2 , suggests that better understanding of the H 2 SO 4 source and sink relationship is needed to accurately predict the atmospheric distributions of H 2 SO 4 .…”

Canopy level emissions of 2-methyl-3-buten-2-ol, monoterpenes, and sesquiterpenes from an experimental Pinus taeda plantation

“…Rather than assign different uncertainties to isoprene and other compounds, it may be more meaningful to assign model uncertainties that range from 30% for a compound emitted at a site that has been characterized to a factor of 5 for a compound emitted from an ecosystem that has not been studied. Aircraft observations across large regions suggest that model predictions of compounds emitted from ecosystems that have been characterized in some way are within a factor of two of observations (Misztal et al, 2014, Warneke et al, 2010.…”