Cracking the chemical code: a data-science approach to deciphering the chemical information stored in environmental samples

破解化学密码：破译环境样本中存储的化学信息的数据科学方法

• 批准号: 1949013
• 负责人: Gerrad Jones
• 金额: $ 32.9万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1949013&HistoricalAwards=false
• 关键词: Cracking; chemical; code; data; science; Cracking; chemical; code; data; science

Numerous natural and anthropogenic processes release organic chemicals into the environment. The underlying hypothesis of this project is that these processes have distinct chemical markers that can be used to uniquely identify each source. Over time, chemicals are transported through the environment and are often captured in lakes, rivers, and the ocean. These water bodies thus contain a chemical record of all processes occurring upstream. This project will develop tools to test water bodies for these chemical markers, using artificial intelligence (AI) tools to screen for all processes that occur in a watershed. This approach holds great promise to efficiently collect more data than existing methods. Successful development of this AI approach will have wide-ranging applications to detect and identify sources of pollution from local to global scales. Broader impacts to society will result from the training of underrepresented communities and development of STEM curricula for high school students. These will lead to diversifying the STEM workforce and increasing scientific literacy. Lakes and other bodies of water can be considered as systems that store chemical information recorded in the form of tens of thousands of molecules in the water and sediment. The goal of this research project is to use artificial intelligence (AI) algorithms to translate the chemical data stored in environmental samples into knowledge about ecosystem processes. This goal will be achieved through specific objectives to: 1) develop diagnostic chemical fingerprints associated with multiple anthropogenic pollution sources, 2) quantify environmental processes the affect the chemical composition in receiving water bodies, and 3) identify pollution sources through source identification in various water bodies including lakes and groundwater. Although current application of chemical forensics is focused on pollution source tracking, the goal of this research is substantially broader. Hundreds to thousands of ecosystem processes occur across the landscape, and successful completion of the research holds promise to track environmental processes through fingerprint identification within a single water sample.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.

许多天然和人为过程将有机化学物质释放到环境中。该项目的基本假设是这些过程具有不同的化学标记，可用于唯一识别每个来源。随着时间的流逝，化学物质通过环境运输，经常被捕获在湖泊，河流和海洋中。因此，这些水体包含上游所有过程的化学记录。该项目将使用人工智能（AI）工具开发工具来测试这些化学标志物的水体，以筛选出流域中发生的所有过程。这种方法具有比现有方法有效收集更多数据的巨大希望。这种AI方法的成功开发将具有广泛的应用程序，以检测和确定从本地到全球范围的污染来源。培训代表性不足的社区和为高中生的STEM课程的发展而对社会产生的更广泛的影响。这些将导致STEM劳动力多样化并提高科学素养。可以将湖泊和其他水体视为存储以水和沉积物中成千上万个分子形式记录的化学信息的系统。该研究项目的目的是使用人工智能（AI）算法将存储在环境样本中的化学数据转化为有关生态系统过程的知识。将通过以下特定目标来实现此目标：1）开发与多个人为污染源相关的诊断化学指纹，2）量化环境过程，量化了接收水体的化学成分，以及3）3）通过在包括湖泊和地下水在内的各种水体中的来源识别来识别污染源。尽管当前的化学取证应用集中在污染源跟踪上，但这项研究的目标大大较广。在整个景观中都发生了数百至数千个生态系统过程，该研究的成功完成有望通过单个水样中的指纹识别来跟踪环境过程。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的审查标准来通过评估来通过评估来获得支持的。