The Development of Optical Classification Models for Ambient Aerosols Using Machine Learning

该项目将开发一种使用机器学习的工具，根据测量的光学特性对环境气溶胶进行分类。聚类方法将用于识别具有相似光学特性的样品，而无需对其身份进行任何先验假设。这些聚类将用于创建能够区分气溶胶类型的分类模型。气溶胶的精确光学分类有助于改进气候模式，为气候模式中如何处理气溶胶提供约束条件。本项目的目标是：（1）识别和定义具有不同光学特性的大气气溶胶团簇;（2）根据目标1的观测结果和团簇建立光学分类模式;（3）转换光学分类模型以用于低成本传感器。推广活动包括使用低成本的传感器来帮助中学生、本科生和研究生直接了解大气中的气溶胶。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project will develop a tool using machine learning to classify ambient aerosols based on measured optical properties. Clustering methods will be used to identify samples with similar optical properties without having to make any a priori assumptions about their identities. The clusters will be used to create classification models that can differentiate aerosol types. The accurate optical classification of aerosols can help to improve climate models by providing constraints on how aerosols are treated in climate models.The objectives of this project are to: (1) identify and define clusters of ambient aerosols with distinct optical properties; (2) build optical classification models based on observations and clusters from Objective 1; (3) translate optical classification models for use with low-cost sensors. Outreach activities include the use of low-cost sensors to help students at the middle school, undergraduate and graduate levels learn firsthand about aerosols in the atmosphere.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.