Advancing Aerosol Science through Data Analysis Tools

A Lagrangian Time-Series Machine Learning Framework for Predicting Concentrations and Exploring Drivers of Cloud Condensation Nuclei in Marine Boundary Layer. Shengqian Zhou, Dong Qi, Hanyang Liu, Chenyang Lu, Yevgeniy Vorobeychik, JIAN WANG, Washington University in St. Louis

A Machine Learning Model for Automating Soot Morphology Analysis from TEM Images. TIMOTHY DAY, Khaled Mosharraf Mukut, Somesh Roy, Marquette University

Agent-Based Framework for Modelling Hyperlocal Air Quality in Urban Spaces. SATHISH SWAMINATHAN, Raghunathan Rengaswamy, V. Faye McNeill, Columbia University

An Analytical Framework for Characterizing Pollutant Source and Sink Mechanisms from Time-Resolved Indoor Air Quality Data. SAEED FARHOODI, Insung Kang, Kaveeta Jagota, Nancy Karpen, Zane Z. Elfessi, Israel Rubinstein, Mohammad Heidarinejad, Brent Stephens, Illinois Institute of Technology

Application of Clustering Algorithms for Improved Source Apportionment with Photothermal Infrared Spectra from Individual Particles Collected in New York City. ABBYGAIL AYALA, Yao Xiao, Eduardo Ochoa Rivera, Kayleigh Reilly, Emily Costa, Xu He, Corin Tyler, Drew Gentner, Rachel O'Brien, Ambuj Tewari, Andrew P. Ault, University of Michigan

Deploying a Comprehensive Air Quality Sensor Network in Elizabeth, NJ: Insights and Early Findings. GEDIMINAS MAINELIS, Catherine J. Hart, Jennifer Senick, Jackie Park-Albaum, John Evangelista, Clinton J. Andrews, Jie Gong, Sunyoung Kim, Abigail Andrews, Holly Josephs, Vijay Maddila, Deborah Plotnik, Jenna Myers, Yitong Li, Ge Gao, Carmen Rosario, Phaneendra Sivangula, Yousaf Shahid, Rutgers, The State University of New Jersey

Development of the US Environmental Protection Agency’s Environmental Source Apportionment Toolkit (ESAT). PHILIP K. HOPKE, Deron Smith, Michael Cyterski, John Johnston, Kurt Wolfe, Rajbir Parmar, U.S. Environmental Protection Agency

High-Resolution PM2.5 Exposure Analysis in Africa: Assessing Long Term Trends for Effective Air Quality Management. PAA SEY, Paulina Jaramillo, Albert Presto, Carnegie Mellon University

Identifying Local Emission Sources via an Integrated Computer Vision and Vision–Language Models (VLMs) Approach. JINTAO GU, Hongpufan Huang, Boyuan Niu, K. Max Zhang, Cornell University

Integrated Screening Techniques Reveal Insight into Non-Traffic Emissions Sources. Michelle S. Hui, JINTAO GU, Timothy Baker, Mohammed I. Mead, K. Max Zhang, Cornell University

Integrating New Facilities for Aerosol Measurements with Eddy Covariance Flux Measurements for Improved Understanding of Atmosphere-Biosphere Interactions. ANDREW METCALF, James Henry, Thomas O'Halloran, Clemson University

Leveraging Satellite Measurements, Surface Monitors, and Machine Learning for Estimating 20 Years of High-Resolution Gridded PM2.5 in Ghana. ABHISHEK ANAND, Joe Adabouk Amooli, Daniel Westervelt, Columbia University

Machine Learning Derivation of Composition-Resolved Particle Number Size Distribution and Aerosol Properties. ARSHAD ARJUNAN NAIR, Fangqun Yu, The State University of New York at Albany

Multiview Conformal Prediction (MVCP) Utilizes Infrared and Raman Spectra for Improved Atmospheric Microplastic Identification. REBECCA L. PARHAM, Eduardo Ochoa Rivera, Madeline E. Clough, Abbygail Ayala, Anne J. McNeil, Ambuj Tewari, Andrew P. Ault, University of Michigan

Particula: Predictive Aerosol Research Through Modular Design and AI Integration. KYLE GORKOWSKI, Naser Mahfouz, Wayne Chaung, Los Alamos National Laboratory

Predicting Current and Future Concentrations of Biogenic Aerosol Precursors Using Machine Learning and Detailed Chemical Modeling. SINA TAYYEBI NIA, Nazifa Sayeed, Namrata Shanmukh Panji, Chenyang Bi, Gabriel Isaacman-VanWertz, Virginia Tech

Quantifying Aerosol Deposition Under Non-standard Environmental Conditions: Combined Experimental Measurement and Computational Modeling Techniques. REN GARITY, James Henry, Moein Mousavi, Andrew Metcalf, Prasad Rangaraju, John Saylor, Clemson University

Revisiting Parameter Inversion of Aerosol and Cloud Chambers. NASER MAHFOUZ, Pacific Northwest National Laboratory

Using Machine Learning to Derive Long-Term Aerosol Liquid Water Concentrations from Aerosol Optical Properties. LIFEI YIN, Bin Bai, Shreya Suri, Yuhan Yang, James Sherman, Robert Swarthout, Pengfei Liu, Georgia Institute of Technology