Improving the simulation of Intermediate Volatility Compounds (IVOCs) in chemical transport models

<p>Secondary organic aerosol (SOA) can be formed in the atmosphere through oxidation of volatile (VOCs), intermediate volatility (IVOCs) and semivolatile organic compounds (SVOCs), and condensation of their less volatile products to the particulate phase. While there has been a lot of progress with the simulation of the VOC chemistry, the simulation of the IVOCs remains challenging. In this study, we develop a new approach for the treatment of these compounds in chemical transport models, treating them as lumped species, similar to the VOCs. The new species are implemented in the SAPRC gas-phase chemical mechanism. We introduce four new lumped species representing larger alkanes, two species for polyaromatic hydrocarbons (PAHs) and one new lumped species representing aromatics, all in the IVOC volatility range. Their gas-phase chemistry is assumed to be analogous to that of the large alkanes and aromatics currently in the SAPRC mechanism but with appropriate parameters. The SOA yields for these additional species were estimated for low and high-NOx conditions following the Volatility Basis Set framework and using the available results of smog chamber studies. As most emission inventories do not include IVOCs, we estimated their emissions starting from road transport using existing non-methane hydrocarbons emissions and emission factors of individual IVOCs from laboratory studies. The total IVOC emissions from diesel vehicles for Europe were significantly higher than those coming from gasoline vehicles. The emissions and extended mechanism were implemented in PMCAMx and were used to simulate the EUCAARI intensive period. Cyclic alkanes, which have both high SOA yields and high emissions, were a major SOA precursor group. The contribution of the various IVOCs to SOA formation, and their overall role is discussed. Significant remaining uncertainties are summarized.</p>

Isoprene photooxidation: new insights into the production of acids and organic nitrates

Atmospheric Chemistry and Physics ◽

10.5194/acp-9-1479-2009 ◽

2009 ◽

Vol 9 (4) ◽

pp. 1479-1501 ◽

Cited By ~ 353

Author(s):

F. Paulot ◽

J. D. Crounse ◽

H. G. Kjaergaard ◽

J. H. Kroll ◽

J. H. Seinfeld ◽

...

Keyword(s):

Carbon Flux ◽

Chemical Transport ◽

Chemical Mechanism ◽

Organic Nitrates ◽

Transport Models ◽

Total Oxidation ◽

Time Resolved ◽

Isoprene Nitrates ◽

Chamber Studies

Abstract. We describe a nearly explicit chemical mechanism for isoprene photooxidation guided by chamber studies that include time-resolved observation of an extensive suite of volatile compounds. We provide new constraints on the chemistry of the poorly-understood isoprene δ-hydroxy channels, which account for more than one third of the total isoprene carbon flux and a larger fraction of the nitrate yields. We show that the cis branch dominates the chemistry of the δ-hydroxy channel with less than 5% of the carbon following the trans branch. The modelled yield of isoprene nitrates is 12&plusmn3% with a large difference between the δ and β branches. The oxidation of these nitrates releases about 50% of the NOx. Methacrolein nitrates (modelled yield ~15±3% from methacrolein) and methylvinylketone nitrates (modelled yield ~11±3% yield from methylvinylketone) are also observed. Propanone nitrate, produced with a yield of 1% from isoprene, appears to be the longest-lived nitrate formed in the total oxidation of isoprene. We find a large molar yield of formic acid and suggest a novel mechanism leading to its formation from the organic nitrates. Finally, the most important features of this mechanism are summarized in a condensed scheme appropriate for use in global chemical transport models.

Analyzing experimental data and model parameters: implications for predictions of SOA using chemical transport models

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-13-15907-2013 ◽

2013 ◽

Vol 13 (6) ◽

pp. 15907-15947 ◽

Cited By ~ 1

Author(s):

K. C. Barsanti ◽

A. G. Carlton ◽

S. H. Chung

Keyword(s):

Air Quality ◽

Transport Model ◽

Chemical Transport ◽

Basis Set ◽

Base Case ◽

Model Parameters ◽

Smog Chamber ◽

Modeling Framework ◽

Transport Models ◽

Abstract. Despite the critical importance for air quality and climate predictions, accurate representation of secondary organic aerosol (SOA) formation remains elusive. An essential addition to the ongoing discussion of improving model predictions is an acknowledgement of the linkages between experimental conditions, parameter optimization and model output, as well as the linkage between empirically-derived partitioning parameters and the physicochemical properties of SOA they represent in models. In this work, advantages of the volatility basis set (VBS) modeling approach are exploited to develop parameters for use in the computationally-efficient and widely-used two product (2p) SOA modeling framework, standard in chemical transport models such as CMAQ (Community Multiscale Air Quality) and GEOS-Chem (Goddard Earth Observing System–Chemistry). Calculated SOA yields and mass loadings obtained using the newly-developed 2p-VBS parameters and existing 2p and VBS parameters are compared with observed yields and mass loadings from a comprehensive list of published smog chamber studies to determine a "best available" set of SOA modeling parameters. SOA and PM2.5 levels are simulated using CMAQv.4.7.1; results are compared for a base case (with default 2p CMAQ parameters) and two "best available" parameter cases chosen to illustrate the high- and low-NOx limits of biogenic SOA formation from monoterpenes. Comparisons of published smog chamber data with SOA yield predictions illustrate that: (1) SOA yields for naphthalene and cyclic and > C5 alkanes are not well represented using either newly developed (2p-VBS) or existing (2p and VBS) parameters for low-yield aromatics and lumped alkanes, respectively; and (2) for 4 of 7 volatile organic compound + oxidant systems, the 2p-VBS parameters better represent existing data. Using the "best available" parameters (combination of published 2p and newly derived 2p-VBS), predicted SOA mass and PM2.5 concentrations increase by up to 10–15% and 7%, respectively, for the high-NOx case and up to 215% (~ 3 μg m−3) and 55%, respectively, for the low-NOx case. The ability to robustly assign "best available" parameters, however, is limited due to insufficient data for photo-oxidation of diverse monoterpenes and sesquiterpenes under a variety of atmospherically relevant NOx conditions. These results are discussed in terms of implications for current chemical transport model simulations and recommendations are provided for future measurement and modeling efforts.

Non-equilibrium interplay between gas-particle partitioning and multiphase chemical reactions of semi-volatile compounds: mechanistic insights and practical implications for atmospheric modeling of PAHs

10.5194/acp-2020-1000 ◽

2020 ◽

Author(s):

Jake Wilson ◽

Ulrich Pöschl ◽

Manabu Shiraiwa ◽

Thomas Berkemeier

Keyword(s):

Mass Transport ◽

Gas Phase ◽

Chemical Reactions ◽

Rate Coefficient ◽

Chemical Transport ◽

Atmospheric Modeling ◽

Time Step ◽

Transport Models ◽

Non Equilibrium

Abstract. Polycyclic aromatic hydrocarbons (PAHs) are carcinogenic air pollutants. The dispersion of PAHs in the atmosphere is influenced by gas-particle partitioning and chemical loss. These processes are closely interlinked and may occur at vastly differing timescales, which complicates their mathematical description in chemical transport models. Here, we use a kinetic model that explicitly resolves mass transport and chemical reactions in the gas and particle phases to describe and explore the dynamic and non-equilibrium interplay of gas-particle partitioning and chemical losses of PAHs on soot particles. We define the equilibration timescale τeq of gas-particle partitioning as the e-folding time for relaxation of the system to the partitioning equilibrium. We find this metric to span seconds to hours depending on temperature, particle surface area and the type of PAH. The equilibration time can be approximated using a time-independent equation τeq &approx; 1/(kdes + kads), which depends on the desorption rate coefficient kdes and adsorption rate coefficient kads, both of which can be calculated from experimentally-accessible parameters. The model reveals two regimes in which different physical processes control the equilibration timescale: a desorption-controlled and an adsorption-controlled regime. In a case study with the PAH pyrene, we illustrate how chemical loss can perturb the equilibrium particulate fraction at typical atmospheric concentrations of O3 and OH. For the surface reaction with O3, the perturbation is significant and increases with the gas-phase concentration of O3. Conversely, perturbations are smaller for reaction with the OH radical, which reacts with PAHs on both the surface of particles and in the gas phase. Global and regional chemical transport models typically approximate gas-particle partitioning with instantaneous equilibration approaches. We highlight scenarios in which these approximations deviate from the explicit-coupled treatment of gas-particle partitioning and chemistry presented in this study. We find that the discrepancy between solutions depends on the operator-splitting time step and the choice of time step can help to minimize the discrepancy. The findings and techniques presented in this work are not only relevant for PAHs, but can also be applied to other semi-volatile substances that undergo chemical reactions and mass transport between the gas and particle phase.

Non-equilibrium interplay between gas–particle partitioning and multiphase chemical reactions of semi-volatile compounds: mechanistic insights and practical implications for atmospheric modeling of polycyclic aromatic hydrocarbons

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-6175-2021 ◽

2021 ◽

Vol 21 (8) ◽

pp. 6175-6198

Author(s):

Jake Wilson ◽

Ulrich Pöschl ◽

Manabu Shiraiwa ◽

Thomas Berkemeier

Keyword(s):

Polycyclic Aromatic Hydrocarbons ◽

Gas Phase ◽

Chemical Reactions ◽

Aromatic Hydrocarbons ◽

Rate Coefficient ◽

Chemical Transport ◽

Time Step ◽

Transport Models ◽

Polycyclic Aromatic

Abstract. Polycyclic aromatic hydrocarbons (PAHs) are carcinogenic air pollutants. The dispersion of PAHs in the atmosphere is influenced by gas–particle partitioning and chemical loss. These processes are closely interlinked and may occur at vastly differing timescales, which complicates their mathematical description in chemical transport models. Here, we use a kinetic model that explicitly resolves mass transport and chemical reactions in the gas and particle phases to describe and explore the dynamic and non-equilibrium interplay of gas–particle partitioning and chemical losses of PAHs on soot particles. We define the equilibration timescale τeq of gas–particle partitioning as the e-folding time for relaxation of the system to the partitioning equilibrium. We find this metric to span from seconds to hours depending on temperature, particle surface area, and the type of PAH. The equilibration time can be approximated using a time-independent equation, τeq≈1kdes+kads, which depends on the desorption rate coefficient kdes and adsorption rate coefficient kads, both of which can be calculated from experimentally accessible parameters. The model reveals two regimes in which different physical processes control the equilibration timescale: a desorption-controlled and an adsorption-controlled regime. In a case study with the PAH pyrene, we illustrate how chemical loss can perturb the equilibrium particulate fraction at typical atmospheric concentrations of O3 and OH. For the surface reaction with O3, the perturbation is significant and increases with the gas-phase concentration of O3. Conversely, perturbations are smaller for reaction with the OH radical, which reacts with pyrene on both the surface of particles and in the gas phase. Global and regional chemical transport models typically approximate gas–particle partitioning with instantaneous-equilibration approaches. We highlight scenarios in which these approximations deviate from the explicitly coupled treatment of gas–particle partitioning and chemistry presented in this study. We find that the discrepancy between solutions depends on the operator-splitting time step and the choice of time step can help to minimize the discrepancy. The findings and techniques presented in this work not only are relevant for PAHs but can also be applied to other semi-volatile substances that undergo chemical reactions and mass transport between the gas and particle phase.

Air quality forecasting system based on chemical transport models

Hydrometeorological research and forecasting ◽

10.37162/2618-9631-2019-4-203-218 ◽

2019 ◽

Vol 4 ◽

pp. 203-218

Author(s):

I.N. Kusnetsova ◽

◽

I.U. Shalygina ◽

M.I. Nahaev ◽

U.V. Tkacheva ◽

...

Keyword(s):

Air Quality ◽

Chemical Transport ◽

Transport Models ◽

Forecasting System ◽

Air Quality Forecasting

Machine learning based bias correction for numerical chemical transport models

Atmospheric Environment ◽

10.1016/j.atmosenv.2020.118022 ◽

2021 ◽

Vol 248 ◽

pp. 118022

Author(s):

Min Xu ◽

Jianbing Jin ◽

Guoqiang Wang ◽

Arjo Segers ◽

Tuo Deng ◽

...

Keyword(s):

Machine Learning ◽

Bias Correction ◽

Chemical Transport ◽

Transport Models ◽

Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

10.20944/preprints201811.0607.v1 ◽

2018 ◽

Author(s):

Scott D. Chambers ◽

Elise-Andree Guérette ◽

Khalia Monk ◽

Alan D. Griffiths ◽

Yang Zhang ◽

...

Keyword(s):

Air Quality ◽

Diurnal Cycle ◽

Transport Model ◽

Chemical Transport ◽

Chemical Transport Model ◽

Transport Models ◽

Potential Health ◽

Meteorological Model ◽

Model Skill ◽

We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO<sub>2</sub> from nadir-viewing satellites by weighted convolution

Atmospheric Measurement Techniques ◽

10.5194/amt-9-2753-2016 ◽

2016 ◽

Vol 9 (7) ◽

pp. 2753-2779 ◽

Cited By ~ 13

Author(s):

Steffen Beirle ◽

Christoph Hörmann ◽

Patrick Jöckel ◽

Song Liu ◽

Marloes Penning de Vries ◽

...

Keyword(s):

Input Data ◽

Polar Vortex ◽

Chemical Transport ◽

Estimation Algorithm ◽

Satellite Observations ◽

Satellite Measurements ◽

Transport Models ◽

Verification Algorithm ◽

Total Column

Abstract. The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm−2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study.