2021 Session Topics and Presenters

Session 1: Molecule to Single Particle

Chairs: Tinja Olenius, Swedish Meteorological and Hydrological Institute & Ivan Piletic, US EPA

The atmosphere is a complex dynamic entity whose composition of gas phase molecules and particles varies considerably in space and time. Large scale atmospheric modeling of the constituents and their effects critically depends on foundational studies of chemical and physical processes occurring on the molecular to single particle level that can accurately account for the properties and diversity. This session highlights work that describes fundamental aerosol processes such as gas phase chemistry and partitioning, nucleation, surface effects, phase state and intraparticle chemistry.

Session Presentations:

• Understanding nucleation and initial particle growth in binary vapors with controlled Laval expansion by Chenxi Li

• A general parametrization for salt nanoparticle formation by Nanna Myllys 

• Atomistic molecular dynamics simulations of ion-dipole collisions in the atmosphere by Bernhard Reischl

• Identification of environmentally relevant isomers based on computational ion mobility predictions by Ivo Neefjes

• A Quantum Machine Learning Approach for Studying Atmospheric Molecular Cluster Formation by Jonas Elm

• Liquid-liquid phase separation in submicron aerosol by Miriam Arak Freedman

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Session 2: Emerging Modelling Techniques

Chairs: Christopher Tessum, University of Illinois & Zhonghua Zheng, Columbia University

This session provides a forum to discuss emerging aerosol modeling techniques. Our motivation is to foster discussion that can enhance the predictive understanding of aerosol, focusing on improving the accuracy and speed of model predictions. We particularly encourage unconventional or early-stage approaches with the potential to transform the practice of aerosol modeling. We welcome submissions from any subfield, including process-based modeling, data-driven modeling, or multi-source data fusion techniques.

Session Presentations:

• Emulating an Aerosol Microphysics Model with Deep Learning by Paula Harder

• Aerosol parameter value tuning within an uncertainty framework by Leighton Regayre

• A Conservative Finite Volume Framework and Implementation for Aggregation, Breakage and Growth Problems on Arbitrary Grids by Daniel O'Sullivan

• Improving global aerosol models through emulation by Duncan Watson-Parris

• Multi-phase chemistry surrogate modeling with elemental mass conservation using a Neural ODE by Xiaokai Yang

• Quantifying the structural uncertainty of the aerosol mixing state representation in MAM4 by Zhonghua Zheng

• AMORE: Automated Mechanism Reduction in Atmospheric Chemistry by Forwood Wiser

• Step-wise Hydration of Organics and Electrolytes in Atmospheric Aerosols by Tony Wexler

• Predicting Glass Transition Temperature and Viscosity of Organic Molecules via Machine Learning and Molecular Embeddings by Tommaso Galeazzo

• Droplet Breakup for the Super-Droplet Method by Emily de Jong

• Machine Learning (ML) Clustering Algorithms as a Receptor Modelling Technique for Source Apportionment of Particulate Matter by Manoranjan Sahu

• Machine learning for aerosols, clouds, and climate: from prototyping to model implementation by Sam Silva

• Super-droplet Method to Simulate Lagrangian Microphysics of Fallout Particles by Dana McGuffin

• Learning coagulation processes with combinatorially-invariant neural networks by Justin Wang

• Lightning Talk Presenter: Maegan DeLessio: Modeling atmospheric brown carbon in the GISS ModelE Earth system model

• Lightning Talk Presenter: Marcel Müller: Accelerating multiphase-chemical kinetics modelling through machine learning metamodels

• Lightning Talk Presenter: Jiachen Liu: Leveraging novel higher-order sensitivity analysis to assess the predictability of background SOA concentrations with a state-of-the-science aerosol mechanism

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Session 3: Air Quality Modeling for Health and Regulatory Assessments

Chairs: Ajith Kaduwela, California Air Resources Board & Ben Murphy, US EPA

The presentations in this session connect the modeling tools and results discussed in prior sessions to regulatory and health assessments. Moreover, this session demonstrates to established and young scientists how their important work is being used by regulators and the policy/health science community to better understand the impact of pollution management efforts. Many of the discussions in this session are also expected to highlight the emerging challenges in particulate matter regulatory and health applications that may be met with advanced aerosol model algorithms.

Session Presentations:

• Pathways of China's PM2.5 air quality 2015-2060 in the context of carbon neutrality by Qiang Zhang

• Issues with condensable organics in European PM2.5 emissions; key messages and follow-up from an expert workshop organised by EMEP MSC-W by David Simpson

• Particulate Matter Regulatory Modeling Applications by Heather Simon

• Integrating reactive organic carbon emissions into the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) by Havala Pye

• Investigating Wildfire Plumes with low-cost air sensors: Implications for grid-based photochemical Air Quality modeling by Ajith Kaduwela

• Lightning Talk Presenter: Saif Shahrukh: Assessment of Air Pollution Tolerance, Anticipated Performance, and Metal Accumulation Indices: Implications for Roadside Planting for Improving the Quality of Urban Air

• Lightning Talk Presenter: Yuliia Yukhymchuk: Pollution in Kyiv by PM 2.5 and 10 during 2020-2021 years: characteristics, and distribution by districts

• Lightning Talk Presenter: Ruqian Miao: Estimating size-specific particulate matter exposure in China based on machine learning parameterizations

• Lightning Talk Presenter: Saif Shahrukh: Capturing of Particulate Matter from Ambient Air by Four Evergreen Tree Species in Dhaka, Bangladesh

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Session 4: Advances in regional and global scale aerosol model developments for simulating the myriad processes affecting the properties and chemical composition of fine particles in the atmosphere

Chairs: Tzung-May Fu, Southern University of Science and Technology & Manish Shrivastava, Pacific Northwest National Laboratory

Simulating the myriad primary and secondary physico-chemical processes that govern the formation and physico-chemical properties of particles in the atmosphere at regional and global scales remains a significant challenge. There are large heterogeneities in processes involved, varying across different spatial and temporal scales. In addition to designing computationally efficient algorithms to represent complex processes, a detailed model evaluation is needed with respect to multiple parameters that affect climate- and health-relevant properties of particles. Development of process-based but computationally efficient algorithms need to be connected to atmospherically relevant measurements so that they can be applied across large temporal and spatial scales. Here we invite recent advances in regional and global-scale modeling of particles relevant to climate and human health, developments of computationally efficient algorithms to represent these processes in models, and their evaluation with laboratory and field measurements.

Session Presentations:

• Global simulations of monoterpene-derived peroxy radical fates and the distributions of highly oxygenated organic molecules and accretion products by Joel Thornton
   

• Implementation of FIREX-AQ measured optical properties in WRF-Chem for estimations of secondary organic aerosol formation by Chenchong Zhang

• Reduction of numerical diffusion in linear advection schemes with application to Eulerian transport models by Robert McGraw

• Chemistry Across Multiple Phases (CAMP): An integrated multi-phase chemistry model by Nicole Riemer

• A 3D particle-resolved model (WRF-PartMC) for quantifying structural uncertainties in a modal model (MAM3) on the regional scale by Jeffrey Curtis

• Process-based and Observation-constrained SOA Simulations in China: The Role of Semivolatile and Intermediate-Volatility Organic Compounds and OH Levels by Qi Chen

• Simulating new-particle formation and the impact on cloud condensation nuclei in pristine and polluted environments of the Amazon by Bin Zhao

• Global budget of atmospheric organic nitrogen aerosols and implications for nitrogen deposition by Yumin Li

• GENOA: the generator of semi-explicit mechanisms for SOA modeling by Zhizhao Wang

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Session 5: Emissions and Sources

Chairs: Kelley Barsanti, UC Riverside & Delphine Farmer, Colorado State University

The Emissions and Sources session is focused on opportunities and advancements in aerosol modeling algorithms with particular emphasis on: 1) primary aerosol emissions, 2) emissions of secondary aerosol precursors, 3) specific aerosol source types (e.g., marine aerosols, biomass burning aerosols), and 4) aerosol processes associated with specific emissions or sources.

Session Presentations:

• Primary and secondary aerosols in and above deciduous forests by Allison Steiner

• Wintertime aerosol formation in mountain basins by Caroline Womack

• Challenges in modeling emissions from fires in the past, present-day, and future by Loretta Mickley

• Predicting SOA Formation from the Athabasca Oil Sands in Northern Alberta by Craig Stroud

• Estimating emissions of sea salt aerosols in polar regions using satellite data and chemical transport modeling by Hannah Horowitz

• Modeling the indoor ultrafine particle dynamics for indoor episodic emission sources considering coagulation effect by Su-Gwang Jeong

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Session 6: Process Models to Box-Model

Chairs: Thomas Berkemeier, Max Planck Institute for Chemistry & Manabu Shiraiwa, UC Irvine

Box models serve various purposes in the atmospheric sciences as they connect the fundamental physical and chemical properties of gases and aerosols with treatments of mass transport and detailed chemical mechanisms. These models enable atmospheric scientists to use results from quantum mechanical calculations, interpret laboratory experiments and provide a process understanding that can be used in larger scale models such as chemical transport models.
This session highlights work about atmospheric reaction mechanisms, multiphase chemistry, gas-particle dynamics, and phase state on the single particle or box model scale.

Session Presentations:

• Modelling new particle formation, secondary aerosol dynamics and aerosol phase-state highlighting the key role of peroxy radical autoxidation by Pontus Roldin

• Multiphase chemistry within Arctic fog droplets explains unexpected growth of Aitken mode particles to CCN sizes by Erik Hoffmann

• Modeling the Tropospheric Multiphase Chemistry of Biomass Burning Trace Compounds Using the Chemical Aqueous Phase Radical Mechanism (CAPRAM) by Lin He

• Exhalation kinetics of surrogate lung fluid particles using experiments and process modelling by Liviana Klein

• Modelling of aerosol coagulation in turbulent flows with Direct Numerical Simulation and Population Balance Modelling by Malamas Tsagkaridis

• Automatic 2-D component classification and lumping for gas-particle partitioning computations by Andreas Zuend

• Lab-to-environment modeling for bridging scales in atmospheric chemistry by V. Faye McNeill

• Modeling Size-distributed Dynamic SOA Partitioning by Rahul Zaveri

• 'Suppression of nucleation and growth of alpha-pinene oxidation products due to isoprene by Meredith Schervish

• 'Intercomparison of aerosol microphysics parameterizations in the MAM aerosol box model by Kai Zhang

• 'Modeling the Role of Peroxy Radicals in Camphene Secondary Organic Aerosol Formation by Jia Jiang

• 'Process-Level Modeling Can Simultaneously Explain Secondary Organic Aerosol Evolution in Chambers and Flow Reactors by Charles He

• Lightning Talk Presenter: Samuel O'Donnell: Process level modeling of vertically resolved new-particle formation at the Southern Great Plains observatory

• Lightning Talk Presenter: Joseph Lilek: A predictive viscosity model for aqueous electrolytes and mixed organic-inorganic aerosol phases

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2021 Keynote: Reduction of large datasets and expensive air quality model calculations through statistical analysis and machine learning.

Daven Henze - Keynote Speaker

Associate Professor, University of Colorado, Boulder

Atmospheric chemists of our generation are frequently faced with the challenges of processing, interpreting, and generating large datasets and increasingly detailed simulations.  Statistical methods have long been used to adjust estimates from air quality models or to estimate pollutant time-series independently from a physics-based model. More recently, machine-learning based approaches are being used to supplement or augment real-time modeling capabilities. This presentation will touch on the ways in which current research has incorporated machine learning methods to reduce model bias, increase model efficiency, and augment the chemical complexity attainable within a reasonable simulation timeframe, as well as approaches used for statistical reduction of complex datasets.  In addition to an overview of the current state of research across the community in these area, I will touch on results from projects that specifically aim to (i) merge PM2.5 datasets for generating improved estimates of pollution exposure (ii) enhance the ability of 3D models to simulate the full set of heterogenous chemistry driving SOA formation from specific VOCs (iii) implement surrogate models for increasing the computational efficiency of chemical data assimilation and forecast systems, (iv) automate the construction of reduced-complexity chemical mechanisms for simulation of atmospheric chemistry, and (v) quickly identify source factors in large aerosol mass-spec datasets.