Modeling regional air quality and climate: improving organic aerosol and aerosol activation processes in WRF/Chem version 3.7.1

Yahya, Khairunnisa; Glotfelty, Timothy; Wang, Kai; Zhang, Yang; Nenes, Athanasios

doi:10.5194/gmd-10-2333-2017

Cited by 14 publications

(7 citation statements)

References 86 publications

(145 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SOA particle volatility in single-precursor systems has been investigated based on the aerosol evaporation after heating (Lopez-Hilfiker et al, 2015;Donahue et al, 2006) by isothermal dilution (Yli-Juuti et al, 2017) or a combination of the two (Louvaris et al, 2017;Cain et al, 2020). Previous works have shown that the oxidation of α-pinene results in low-volatility and semi-volatile components (Zhang et al, 2015), while extremely low-volatility material could be formed from oligomerization reactions (Lopez-Hilfiker et al, 2015).…”

mentioning

confidence: 99%

Exploring the composition and volatility of secondary organic aerosols in mixed anthropogenic and biogenic precursor systems

et al. 2021

View full text Add to dashboard Cite

Abstract. Secondary organic aerosol (SOA) formation from mixtures of volatile precursors may be influenced by the molecular interactions of the components of the mixture. Here, we report measurements of the volatility distribution of SOA formed from the photo-oxidation of o-cresol, α-pinene, and their mixtures, representative anthropogenic and biogenic precursors, in an atmospheric simulation chamber. The combination of two independent thermal techniques (thermal denuder, TD, and the Filter Inlet for Gases and Aerosols coupled to a high-resolution time-of-flight chemical ionization mass spectrometer, FIGAERO-CIMS) to measure the particle volatility, along with detailed gas- and particle-phase composition measurements, provides links between the chemical composition of the mixture and the resultant SOA particle volatility. The SOA particle volatility obtained by the two independent techniques showed substantial discrepancies. The particle volatility obtained by the TD was wider, spanning across the LVOC and SVOC range, while the respective FIGAERO-CIMS derived using two different methods (i.e. calibrated Tmax and partitioning calculations) was substantially higher (mainly in the SVOC and IVOC, respectively) and narrow. Although the quantification of the SOA particle volatility was challenging, both techniques and methods showed similar trends, with the volatility of the SOA formed from the photo-oxidation of α-pinene being higher than that measured in the o-cresol system, while the volatility of the SOA particles of the mixture was between those measured at the single-precursor systems. This behaviour could be explained by two opposite effects, the scavenging of the larger molecules with lower volatility produced in the single-precursor experiments that led to an increase in the average volatility and the formation of unique-to-the-mixture products that had higher O:C, MW, OSc‾ and, consequently, lower volatility compared to those derived from the individual precursors. We further discuss the potential limitations of FIGAERO-CIMS to report quantitative volatilities and their implications for the reported results, and we show that the particle volatility changes can be qualitatively assessed, while caution should be taken when linking the chemical composition to the particle volatility. These results present the first detailed observations of SOA particle volatility and composition in mixed anthropogenic and biogenic systems and provide an analytical context that can be used to explore particle volatility in chamber experiments.

show abstract

mentioning

confidence: 99%

Exploring the composition and volatility of secondary organic aerosols in mixed anthropogenic and biogenic precursor systems

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Journal of Advances in Modeling Earth Systems mentation branching ratio increases in their study. Previous studies using this scheme implemented in the regional Weather Research and Forecasting model with Chemistry (WRF-Chem) suggested that organic aerosol (OA) loadings in the eastern United States and central Mexico could be reduced by a factor of 2 when the fragmentation branching ratio increases 40% (Shrivastava et al, 2013;Yahya et al, 2017).…”

Section: 1029/2020ms002266mentioning

confidence: 99%

“…Previous modeling studies report a large sensitivity of simulated SOA to the branching ratio between functionalization and fragmentation (Glotfelty et al, 2017;Hermansson et al, 2014;Murphy et al, 2012;Shrivastava et al, 2013Shrivastava et al, , 2015Yahya et al, 2017). Using the Community Atmosphere Model Version 5 (CAM5) incorporated within the Community Earth System Model (CESM), Shrivastava et al (2015) estimated that the global SOA production decreases from 502 to 87 Tg/yr when empirical SOA formulations turn gas-phase fragmentation on.…”

Section: Introductionmentioning

confidence: 99%

New SOA Treatments Within the Energy Exascale Earth System Model (E3SM): Strong Production and Sinks Govern Atmospheric SOA Distributions and Radiative Forcing

Lou

Shrivastava

Easter

et al. 2020

J Adv Model Earth Syst

View full text Add to dashboard Cite

Secondary organic aerosols (SOA) are large contributors to fine particle mass loading and number concentration and interact with clouds and radiation. Several processes affect the formation, chemical transformation, and removal of SOA in the atmosphere. For computational efficiency, global models use simplified SOA treatments, which often do not capture the dynamics of SOA formation. Here we test more complex SOA treatments within the global Energy Exascale Earth System Model (E3SM) to investigate how simulated SOA spatial distributions respond to some of the important but uncertain processes affecting SOA formation, removal, and lifetime. We evaluate model predictions with a suite of surface, aircraft, and satellite observations that span the globe and the full troposphere. Simulations indicate that both a strong production (achieved here by multigenerational aging of SOA precursors that includes moderate functionalization) and a strong sink of SOA (especially in the middle upper troposphere, achieved here by adding particle-phase photolysis) are needed to reproduce the vertical distribution of organic aerosol (OA) measured during several aircraft field campaigns; without this sink, the simulated middle upper tropospheric OA is too large. Our results show that variations in SOA chemistry formulations change SOA wet removal lifetime by a factor of 3 due to changes in horizontal and vertical distributions of SOA. In all the SOA chemistry formulations tested here, an efficient chemical sink, that is, particle-phase photolysis, was needed to reproduce the aircraft measurements of OA at high altitudes. Globally, SOA removal rates by photolysis are equal to the wet removal sink, and photolysis decreases SOA lifetimes from 10 to~3 days. A recent review of multiple field studies found no increase in net OA formation over and downwind biomass burning regions, so we also tested an alternative, empirical SOA treatment that increases primary organic aerosol (POA) emissions near source region and converts POA to SOA with an aging time scale of 1 day. Although this empirical treatment performs surprisingly well in simulating OA loadings near the surface, it overestimates OA loadings in the middle and upper troposphere compared to aircraft measurements, likely due to strong convective transport to high altitudes where wet removal is weak. The default improved model formulation (multigenerational aging with moderate fragmentation and photolysis) performs much better than the empirical treatment in these regions. Differences in SOA treatments greatly affect the SOA direct radiative effect, which ranges

show abstract

“…The Weather Research and Forecasting model with Chemistry (WRF/Chem) was originally co-developed by U.S. National Center for Atmospheric Research and National Oceanic and Atmospheric Administration and further developed by scientific communities [32]. WRF/Chem v3.7.1 =by the North Carolina State University (NCSU) (WRF/Chem-NCSU) is based on the NCSU's version of WRF/Chem v3.7.1 [41,42]. WRF/Chem v3.7.1-ROMS is based on the NCSU's version of WRF/Chem v3.7.1 coupled with the Regional Ocean Modeling System (ROMS) (WRF/Chem-ROMS) [40].…”

Section: Model Descriptionmentioning

confidence: 99%

Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions

et al. 2019

Self Cite

View full text Add to dashboard Cite

Air pollution and associated human exposure are important research areas in Greater Sydney, Australia. Several field campaigns were conducted to characterize the pollution sources and their impacts on ambient air quality including the Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2), and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). In this work, the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are applied during these field campaigns to assess the models’ capability in reproducing atmospheric observations. The model simulations are performed over quadruple-nested domains at grid resolutions of 81-, 27-, 9-, and 3-km over Australia, an area in southeastern Australia, an area in New South Wales, and the Greater Sydney area, respectively. A comprehensive model evaluation is conducted using surface observations from these field campaigns, satellite retrievals, and other data. This paper evaluates the performance of WRF/Chem-ROMS and its sensitivity to spatial grid resolutions. The model generally performs well at 3-, 9-, and 27-km resolutions for sea-surface temperature and boundary layer meteorology in terms of performance statistics, seasonality, and daily variation. Moderate biases occur for temperature at 2-m and wind speed at 10-m in the mornings and evenings due to the inaccurate representation of the nocturnal boundary layer and surface heat fluxes. Larger underpredictions occur for total precipitation due to the limitations of the cloud microphysics scheme or cumulus parameterization. The model performs well at 3-, 9-, and 27-km resolutions for surface O3 in terms of statistics, spatial distributions, and diurnal and daily variations. The model underpredicts PM2.5 and PM10 during SPS1 and MUMBA but overpredicts PM2.5 and underpredicts PM10 during SPS2. These biases are attributed to inaccurate meteorology, precursor emissions, insufficient SO2 conversion to sulfate, inadequate dispersion at finer grid resolutions, and underprediction in secondary organic aerosol. The model gives moderate biases for net shortwave radiation and cloud condensation nuclei but large biases for other radiative and cloud variables. The performance of aerosol optical depth and latent/sensible heat flux varies for different simulation periods. Among all variables evaluated, wind speed at 10-m, precipitation, surface concentrations of CO, NO, NO2, SO2, O3, PM2.5, and PM10, aerosol optical depth, cloud optical thickness, cloud condensation nuclei, and column NO2 show moderate-to-strong sensitivity to spatial grid resolutions. The use of finer grid resolutions (3- or 9-km) can generally improve the performance for those variables. While the performance for most of these variables is consistent with that over the U.S. and East Asia, several differences along with future work are identified to pinpoint reasons for such differences.

show abstract

Modeling regional air quality and climate: improving organic aerosol and aerosol activation processes in WRF/Chem version 3.7.1

Cited by 14 publications

References 86 publications

Exploring the composition and volatility of secondary organic aerosols in mixed anthropogenic and biogenic precursor systems

Exploring the composition and volatility of secondary organic aerosols in mixed anthropogenic and biogenic precursor systems

New SOA Treatments Within the Energy Exascale Earth System Model (E3SM): Strong Production and Sinks Govern Atmospheric SOA Distributions and Radiative Forcing

Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions

Contact Info

Product

Resources

About