Multispectral Sensor for Chemical Composition Analysis of Ultrafine Aerosols in Air Quality Assessment

项目摘要 我们建议开发和验证一种新的平台技术，该技术将集合和 使用原位多光谱技术对超细颗粒进行化学分析。直接采集的样本 通过激发-发射矩阵（EEM）光谱分析。这种方法将 经验证可对抗实验室燃烧产生的气溶胶，如柴油机尾气、木材烟雾、烟草 烟雾，以及环境污染物的混合物。在呼吸道内，颗粒大小决定了 沉积和组织吸收的区域;颗粒的化学性质也影响溶解度并决定 可能与组织和细胞发生生化反应。人们越来越意识到， 是非常复杂的，包括随时间变化的浓度和化学成分在广泛的范围内， 颗粒大小。长期暴露在空气污染中也与传染病死亡率的增加有关。 疾病，包括COVID-19。拟议的研究解决了改善个人暴露的需要 环境中超细颗粒的评估和表征。低成本、小型化曝光 监测设备可以使人们深入了解接触污染物与健康影响之间的关系。 源分配测量PM浓度具有高的时间和空间分辨率， 实施最佳空气污染缓解战略。该项目的预期成果是 开发了一种小型化光谱传感器，用于分析 燃烧产生的超细颗粒物，既反映了颗粒物的来源，又决定了它们的毒性 潜力机器学习算法将使得能够对复杂光谱进行去卷积， 从EEM分析中识别PM源。该技术更广泛的应用是 环境和监管监测，消费者市场的个人暴露评估，以及 流行病学研究。

Project Summary We propose developing and validating a novel platform technology that combines the collection and chemical analysis of ultrafine particles using an in-situ multispectral technique. The sample, collected directly onto the analysis substrate, is analyzed via excitation-emission matrix (EEM) spectroscopy. This approach will be validated against laboratory combustion-generated aerosols, such as diesel exhaust, wood smoke, tobacco smoke, and against a mixture of environmental pollutants. Within the respiratory tract, particle size determines the region of deposition and tissue uptake; the chemistry of the particle also affects solubility and determines the potential for biochemical reaction with tissues and cells. There is a growing awareness that exposure scenarios are very complex, consisting of time-varying concentrations and chemical composition over a broad range of particle sizes. Long-term exposure to air pollution has also been linked to increased mortality rates for infectious diseases, including COVID-19. The proposed research addresses the need for improved personal exposure assessment and characterization of ultrafine particles in the environment. Low-cost, miniaturized exposure monitoring devices can shed insight into the relationships between exposure to pollutants and health impact. Source apportioned measurements of PM concentration with high temporal and spatial resolution can facilitate the implementation of optimal air pollution mitigation strategies. The anticipated outcome of this project is the development of a miniaturized spectroscopic sensor that provides an analysis of the chemical composition of combustion-generated ultrafine particles, which both reflects the particle sources and determines their toxic potential. The machine-learning algorithms will enable the deconvolution of the complex spectra and identification of the PM source from the EEM analysis. The broader applications of the technology are environmental and regulatory monitoring, personal exposure assessment for the consumer market, and epidemiological studies.