Program Topics

2021 Session Topics and Presenters

Session 1: Molecule to Single Particle

Chairs: Tinja Olenius, Swedish Meteorological and Hydrological InstituteIvan 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:

Session 2: Emerging Modelling Techniques

Chairs: Christopher Tessum, University of IllinoisZhonghua 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:

Session 3: Air Quality Modeling for Health and Regulatory Assessments

Chairs: Ajith Kaduwela, California Air Resources BoardBen 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:

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 TechnologyManish 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:

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:

Session 6: Process Models to Box-Model

Chairs: Thomas Berkemeier, Max Planck Institute for ChemistryManabu 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:

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.