Simulation of the chemical evolution of biomass burning organic aerosol

Theodoritsi, Georgia N.; Pandis, Spyros Ν.

doi:10.5194/acp-19-5403-2019

Cited by 24 publications

(32 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…S1c). Details about these predictions can be found in Fountoukis et al (2011;2014) and Theodoritsi and Pandis (2018). 240…”

Section: Pmcamx-sr Resultsmentioning

confidence: 98%

See 1 more Smart Citation

Positive Matrix Factorization of Organic Aerosol: Insights from a Chemical Transport Model

Drosatou¹,

Skyllakou²,

Theodoritsi³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Factor analysis of Aerosol Mass Spectrometer measurements (organic aerosol mass spectra) is often used to determine the sources of organic aerosol (OA). In this study we aim to gain insights regarding the ability of positive matrix factorization (PMF) to identify and quantify the OA sources accurately. We performed PMF and multilinear engine (ME-2) analysis on the predictions of a state-of-the-art chemical transport model (PMCAMx-SR) during a photochemically active period for specific sites in Europe in an effort to interpret the diverse factors usually identified by PMF analysis of field measurements. Our analysis used the predicted concentrations of 27 OA components, assuming that each of them is <q>chemically different</q> from the others. The PMF results based on the chemical transport model predictions are quite consistent (same number of factors and source types) with those of the analysis of AMS measurements. The estimated uncertainty of the contribution of fresh biomass burning is less than 30&#8201;% and of the other primary sources less than 40&#8201;%, when these sources contribute more than 20&#8201;% to the total OA. For contributions between 10 and 20&#8201;% the corresponding uncertainties increase to 50&#8201;%. Finally, when these sources are small (less than 10&#8201;% of the OA) the corresponding error is a factor of two or even three. One of the major questions in PMF analysis of AMS measurements concerns the sources of the two or more oxygenated OA (OOA) factors often reported in field studies. Our analysis suggests that these factors include secondary OA compounds from a variety of anthropogenic and biogenic sources and do not correspond to specific sources. Their characterization in the literature as low and high volatility factors is probably misleading, because they have overlapping volatility distributions. However, the average volatility of the one often characterized as low-volatility factor is indeed lower than that of the other (high volatility factor). Based on the analysis of the PMCAMx-SR predictions, the first oxygenated OA factor includes mainly highly-aged OA transported from outside Europe, but also highly aged secondary OA from precursors emitted in Europe. The second oxygenated OA factor contains fresher SOA from volatile, semi-volatile, and intermediate volatility anthropogenic and biogenic organic compounds. The exact contribution of these OA components to each OA factor depends on the site and the prevailing meteorology during the analysis period.

show abstract

“…S1c). Details about these predictions can be found in Fountoukis et al (2011;2014) and Theodoritsi and Pandis (2018). 240…”

Section: Pmcamx-sr Resultsmentioning

confidence: 98%

“…The model used in this study is the three-dimensional regional CTM PMCAMx-SR 125 (Theodoritsi and Pandis, 2018), a regional three-dimensional CTM. PMCAMx-SR was applied to a 5400×5832 km 2 region covering Europe with 36×36 km grid resolution and 14 vertical layers extending up to 6 km.…”

Section: Pmcamx-srmentioning

confidence: 99%

Positive Matrix Factorization of Organic Aerosol: Insights from a Chemical Transport Model

Drosatou¹,

Skyllakou²,

Theodoritsi³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Various tools have been developed and implemented in AQMs for regional-scale source apportionment of particulate matter, such as the Particulate Source Apportionment Technology (PSAT) for CAMx (Comprehensive Air quality Model with Extensions) (Koo et al, 2009), tagged species source apportionment (TSSA) (Wang et al, 2009) and the integrated source apportionment method (ISAM) (Kwok et al, 2013) for CMAQ (Community Multiscale Air Quality). However, the performance of these tools for OA source apportionment are always limited by substantial underestimation of SOA by the traditional AQMs (Hodzic et al, 2010;Tsigaridis et al, 2014). One of the most important reasons for the underestimation is the potentially high but unaccounted for contribution of non-traditional vapors.…”

Section: Introductionmentioning

confidence: 99%

Sources of organic aerosols in Europe: a modeling study using CAMx with modified volatility basis set scheme

et al. 2019

View full text Add to dashboard Cite

Abstract. Source apportionment of organic aerosols (OAs) is of great importance to better understand the health impact and climate effects of particulate matter air pollution. Air quality models are used as potential tools to identify OA components and sources at high spatial and temporal resolution; however, they generally underestimate OA concentrations, and comparisons of their outputs with an extended set of measurements are still rare due to the lack of long-term experimental data. In this study, we addressed such challenges at the European level. Using the regional Comprehensive Air Quality Model with Extensions (CAMx) and a volatility basis set (VBS) scheme which was optimized based on recent chamber experiments with wood burning and diesel vehicle emissions, and which contains more source-specific sets compared to previous studies, we calculated the contribution of OA components and defined their sources over a whole-year period (2011). We modeled separately the primary and secondary OA contributions from old and new diesel and gasoline vehicles, biomass burning (mostly residential wood burning and agricultural waste burning excluding wildfires), other anthropogenic sources (mainly shipping, industry and energy production) and biogenic sources. An important feature of this study is that we evaluated the model results with measurements over a longer period than in previous studies, which strengthens our confidence in our modeled source apportionment results. Comparison against positive matrix factorization (PMF) analyses of aerosol mass spectrometric measurements at nine European sites suggested that the modified VBS scheme improved the model performance for total OA as well as the OA components, including hydrocarbon-like (HOA), biomass burning (BBOA) and oxygenated components (OOA). By using the modified VBS scheme, the mean bias of OOA was reduced from −1.3 to −0.4 µg m−3 corresponding to a reduction of mean fractional bias from −45 % to −20 %. The winter OOA simulation, which was largely underestimated in previous studies, was improved by 29 % to 42 % among the evaluated sites compared to the default parameterization. Wood burning was the dominant OA source in winter (61 %), while biogenic emissions contributed ∼ 55 % to OA during summer in Europe on average. In both seasons, other anthropogenic sources comprised the second largest component (9 % in winter and 19 % in summer as domain average), while the average contributions of diesel and gasoline vehicles were rather small (∼ 5 %) except for the metropolitan areas where the highest contribution reached 31 %. The results indicate the need to improve the emission inventory to include currently missing and highly uncertain local emissions, as well as further improvement of VBS parameterization for winter biomass burning. Although this study focused on Europe, it can be applied in any other part of the globe. This study highlights the ability of long-term measurements and source apportionment modeling to validate and improve emission inventories, and identify sources not yet properly included in existing inventories.

show abstract

“…This normalization makes the EnR for freshly emitted aerosol equal to unity. In particular, considerable increases (in many cases exceeding a factor of 2) in EnR were found in smog chamber experiments after a few hours of photochemical aging of smoke from wood or grass burning (e.g., Grieshop et al, 2009;Hennigan et al, 2011;Tiitta et al, 2016;Ciarelli et al, 2017a;Fang et al, 2017;Ahern et al, 2019), although there has been a large diversity between results of individual chamber experiments. As a result of aircraft experiments conducted in North America around Mexico City and on the Yucatan Peninsula, significant increases in EnR have been reported by DeCarlo et al (2008) and Yokelson et al (2009) for aging BB plumes (from about 30 % up to a factor of 2).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear behavior of organic aerosol in biomass burning plumes: a microphysical model analysis

et al. 2019

View full text Add to dashboard Cite

Abstract. Organic aerosol (OA) is a major component of smoke plumes from open biomass burning (BB). Therefore, adequate representation of the atmospheric transformations of BB OA in chemistry-transport and climate models is an important prerequisite for accurate estimates of the impact of BB emissions on air quality and climate. However, field and laboratory studies of atmospheric transformations (aging) of BB OA have yielded a wide diversity of observed effects. This diversity is still not sufficiently understood and thus not addressed in models. As OA evolution is governed by complex nonlinear processes, it is likely that at least a part of the observed variability in the BB OA aging effects is due to the factors associated with the intrinsic nonlinearity of the OA system. In this study, we performed a numerical analysis in order to gain a deeper understanding of these factors. We employ a microphysical dynamic model that represents gas–particle partitioning and OA oxidation chemistry within the volatility basis set (VBS) framework and includes a schematic parameterization of BB OA dilution due to dispersion of an isolated smoke plume. Several VBS schemes of different complexity, which have been suggested in the literature to represent BB OA aging in regional and global chemistry-transport models, are applied to simulate BB OA evolution over a 5 d period representative of the BB aerosol lifetime in the dry atmosphere. We consider the BB OA mass enhancement ratio (EnR), which is defined as the ratio of the mass concentration of BB OA to that of an inert tracer and allows us to eliminate the linear part of the dilution effects. We also analyze the behavior of the hygroscopicity parameter, κ, that was simulated in a part of our numerical experiments. As a result, five qualitatively different regimes of OA evolution are identified, which comprise (1) a monotonic saturating increase in EnR, (2) an increase in EnR followed by a decrease, (3) an initial rapid decrease in EnR followed by a gradual increase, (4) an EnR increase between two intermittent stages of its decrease, or (5) a gradual decrease in EnR. We find that the EnR for BB aerosol aged from a few hours to a few tens of hours typically increases for larger initial sizes of the smoke plume (and therefore smaller dilution rates) or for lower initial OA concentrations (and thus more organic gases available to form secondary OA – SOA). However, these dependencies can be weakened or even reversed, depending on the BB OA age and on the ratio between the fragmentation and functionalization oxidation pathways. Nonlinear behavior of BB OA is also exhibited in the dependencies of κ on the parameters of the plume. Application of the different VBS schemes results in large quantitative and qualitative differences between the simulations, although our analysis suggests also that the main qualitative features of OA evolution simulated with a complex two-dimensional VBS scheme can also be reproduced with a much simpler scheme. Overall, this study indicates that the BB aerosol evolution may strongly depend on parameters of the individual BB smoke plumes (such as the initial organic aerosol concentration and plume size) that are typically not resolved in chemistry-transport models.

show abstract

Simulation of the chemical evolution of biomass burning organic aerosol

Cited by 24 publications

References 50 publications

Positive Matrix Factorization of Organic Aerosol: Insights from a Chemical Transport Model

Positive Matrix Factorization of Organic Aerosol: Insights from a Chemical Transport Model

Sources of organic aerosols in Europe: a modeling study using CAMx with modified volatility basis set scheme

Nonlinear behavior of organic aerosol in biomass burning plumes: a microphysical model analysis

Contact Info

Product

Resources

About