Collaborative Research: Linking Dissolved Organic Matter Composition to Photochemical Reactivity

杀虫剂和药物等污染物通常存在于湖泊、河流和其他地表水中。重要的是要了解这些化学品在自然过程中在环境中分解的速度，以便评估它们对人类和生态系统的潜在风险。这项研究项目研究了阳光在被称为光降解的过程中分解农药和药物的作用。光降解是由溶解有机物(DOM)的光化学反应驱动的，DOM是一种从植物和微生物中提取的有机化合物的复杂混合物，存在于所有自然水域中。DOM的化学成分在不同的水域中有很大的不同，不同的成分会影响农药和药物在阳光下分解的速度。因此，该项目将开发一个框架来预测从河流、沼泽、湖泊和位于森林、农业和城市分水岭的废水处理厂收集的各种DOM样本的相对光反应性。通过建立基于DOM组成测量的预测模型，这项研究将为预测化学品在水生系统中的降解率奠定基础。如果成功，这一知识可以用来识别和分离国家自然水域中的化学污染物，从而保护公众健康和环境。该项目旨在确定溶解有机物(DOM)的组成与其光化学反应活性之间的联系。虽然最先进的技术，如高分辨率质谱仪提供了比以往任何时候更多的关于DOM组成的信息，但由于这种复杂混合物的异质性，仅基于其组成来预测DOM的反应性仍然是不可能的。威斯康星大学麦迪逊分校和圣托马斯大学的这项研究合作将检验一种假设，即从各种分析技术得出的参数组合可以用来预测不同DOM样本中的相对光化学反应。主要研究人员假设，不同技术产生的数据的子集对DOM反应性的差异有很大影响，因为每种技术都对不同的DOM群体进行了采样。为了验证这一假设，将从五个流域(圣路易斯河、雅哈拉、北部高地、明尼苏达州河和密西西比河)采集河流和湖泊样本，以及五个污水处理厂消毒前和消毒后的样本。总体而言，这些地点代表了受农药和药品等极地污染物影响的天然淡水的范围。这些样品将通过紫外可见光谱、抗氧化剂测量以及傅立叶变换离子回旋共振(FT-ICR)和Orbitrap质谱仪进行表征。将使用探针研究来量化活性物种的量子产率和稳态浓度，并通过使用五种目标杀虫剂和药物进行实验来评估光反应性。多元统计，包括多元线性回归、层次聚类分析和主成分分析，将被用来开发一种新的框架，以基于组成来预测天然和流出有机物的相对光化学反应活性。最后，这一独特的数据集将被用来测试Orbitrap质谱仪是否能够提供足够的信息，以取代使用FT-ICR质谱仪来确定反应性研究中DOM的分子组成。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Contaminants such as pesticides and pharmaceuticals are commonly found in lakes, rivers, and other surface waters. It is important to know how quickly these chemicals break down in the environment due to natural processes in order to assess their potential risks to humans and ecosystems. This research project studies the role of sunlight in the breakdown of pesticides and pharmaceuticals in a process known as photodegradation. Photodegradation is driven by the photochemical reactions of dissolved organic matter (DOM), which is a complex mixture of organic chemicals derived from plants and microorganisms that is found in all natural waters. The chemical composition of DOM is highly variable in different waters, and the different compositions influence how quickly pesticides and pharmaceuticals break down when exposed to sunlight. Therefore, this project will develop a framework to predict the relative photoreactivity of a wide variety of DOM samples collected from rivers, bogs, lakes, and wastewater treatment plants located in forested, agricultural, and urban watersheds. By developing predictive models based on DOM composition measurements, this research will lay the groundwork for predicting the degradation rates of chemicals in aquatic systems. If successful, this knowledge can be used to identify and isolate chemical contaminants in the Nation's natural waters, thereby protecting public health and the environment. This project aims to determine the link between the composition of dissolved organic matter (DOM) to its photochemical reactivity. While state-of-the-science techniques such as high-resolution mass spectrometry provide more information about DOM composition than ever before, it is still not possible to predict the reactivity of DOM based solely on its composition due to the heterogeneity of this complex mixture. This research collaboration between the University of Wisconsin-Madison and the University of St. Thomas will test the hypothesis that a combination of parameters derived from a variety of analytical techniques can be used to predict relative photochemical reactivity within a diverse set of DOM samples. The principal investigators hypothesize that a subset of the data produced by the different techniques contributes strongly to variance in DOM reactivity because each technique samples different populations of DOM. In order to test this hypothesis, river and lake samples will be collected from five watersheds (St. Louis River, Yahara, Northern Highlands, Minnesota River, and Mississippi River), as well as samples before and after disinfection at five wastewater treatment plants. Collectively, these sites represent the range of natural freshwaters that are impacted by polar contaminants, such as pesticides and pharmaceuticals. These samples will be characterized by UV-visible spectroscopy, antioxidant measurements, and both Fourier transform-ion cyclotron resonance (FT-ICR) and Orbitrap mass spectrometry. Photoreactivity will be assessed using probe studies to quantify the quantum yields and steady-state concentrations of reactive species and by experiments with five target pesticides and pharmaceuticals. Multivariate statistics, including multiple linear regressions, hierarchical cluster analysis, and principal component analysis, will be used to develop a novel framework to predict the relative photochemical reactivity of natural and effluent organic matter based on composition. Finally, this unique data set will be used to test whether Orbitrap mass spectrometry is able to provide sufficient information to replace the use of FT-ICR mass spectrometry in determining the molecular composition of DOM for reactivity studies.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.