Collaborative Research: Linking Dissolved Organic Matter Composition to Photochemical Reactivity

化学物质，如杀虫剂和药品，通常存在于湖泊、河流和其他地表水中。为了评估它们对人类和生态系统的潜在风险，了解这些化学物质在自然过程中在环境中分解的速度是很重要的。该研究项目研究了阳光在农药和药物降解过程中的作用，这一过程被称为光降解。这个过程是由溶解有机物（DOM）的光化学反应驱动的，DOM是一种复杂的有机化学物质混合物，来源于植物和微生物，存在于所有天然水域中。DOM的化学成分在不同的水域变化很大，这些成分影响杀虫剂和药物暴露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森林、农业和城市流域的河流、沼泽、湖泊和污水处理厂收集的各种DOM样品的相对光反应性。通过建立基于DOM组成测量的预测模型，本研究将为预测水生系统中化学物质的降解速率奠定基础。如果成功，这些知识可以用来识别和隔离国家自然水域中的化学污染物，从而保护环境和公众健康。该项目旨在将溶解有机物（DOM）的组成与其光化学反应性联系起来。虽然高分辨率质谱等最先进的技术提供了比以往更多的关于DOM组成的信息，但由于这种复杂混合物的异质性，仅根据其组成预测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之间的这项研究合作将测试一个假设，即从各种分析技术中获得的参数组合可以用来预测不同DOM样品中的相对光化学反应性。主要研究人员假设，由不同技术产生的数据子集对DOM反应性的差异有很大影响，因为每种技术都采样了不同的DOM总体。为了验证这一假设，将从五个流域（圣路易斯河、雅哈拉河、北部高地、明尼苏达河和密西西比河）收集河流和湖泊样本，以及五个污水处理厂消毒前后的样本。总的来说，这些地点代表了受极地污染物（如杀虫剂和药品）影响的天然淡水的范围。这些样品将通过紫外可见光谱、抗氧化测量、傅里叶变换离子回旋共振（FT-ICR）和Orbitrap质谱进行表征。光反应性将通过探针研究来评估，以量化反应物种的量子产率和稳态浓度，并通过五种目标杀虫剂和药物的实验来评估。多元统计，包括多元线性回归，层次聚类分析和主成分分析，将用于开发一个新的框架来预测基于成分的天然和流出有机物质的相对光化学反应性。最后，这个独特的数据集将用于测试Orbitrap质谱法是否能够提供足够的信息来取代FT-ICR质谱法来确定DOM的分子组成以进行反应性研究。该奖项反映了美国国家科学基金会的法定使命，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。

Chemicals, 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 degradation of pesticides and pharmaceuticals in a process known as photodegradation. This process 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 these 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 the environment and public health. This project aims to link the composition of dissolved organic matter (DOM) to its photochemical reactivity. While state-of-the-art 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.