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

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.

许多自然和人为过程向环境中释放有机化学品。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。随着时间的推移，化学品通过环境运输，并经常在湖泊，河流和海洋中捕获。因此，这些水体含有上游发生的所有过程的化学记录。该项目将开发工具来测试水体中的这些化学标记物，使用人工智能（AI）工具来筛选流域中发生的所有过程。这种方法比现有方法更有希望有效地收集更多的数据。这种人工智能方法的成功开发将具有广泛的应用，以检测和识别从地方到全球范围的污染源。对代表性不足的社区的培训和为高中生开发STEM课程将对社会产生更广泛的影响。这将导致STEM劳动力的多样化和科学素养的提高。湖泊和其他水体可以被认为是储存化学信息的系统，这些信息以水和沉积物中数万个分子的形式记录下来。该研究项目的目标是使用人工智能（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。这一目标将通过以下具体目标来实现：1）开发与多种人为污染源相关的诊断化学指纹，2）量化影响接收水体化学成分的环境过程，以及3）通过识别包括湖泊和地下水在内的各种水体中的污染源来识别污染源。虽然目前化学法医学的应用主要集中在污染源追踪上，但这项研究的目标要广泛得多。该研究的成功完成有望通过单一水样的指纹识别来跟踪环境过程。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。

项目成果

Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data

• DOI: 10.1021/acs.est.1c06655
• 发表时间: 2022-04-05
• 期刊: ENVIRONMENTAL SCIENCE & TECHNOLOGY
• 影响因子: 11.4
• 作者: Davila-Santiago, Emmanuel;Shi, Cheng;Jones, Gerrad D.
• 通讯作者: Jones, Gerrad D.

Nontarget Chemical Composition of Surface Waters May Reflect Ecosystem Processes More than Discrete Source Contributions

地表水的非目标化学成分可能比离散源的贡献更能反映生态系统过程

• DOI: 10.1021/acs.est.2c08540
• 发表时间: 2023
• 期刊: Environmental Science & Technology
• 影响因子: 11.4
• 作者: Shi, Cheng;Mahadwar, Gouri;Dávila-Santiago, Emmanuel;Bambakidis, Ted;Crump, Byron C.;Jones, Gerrad D.
• 通讯作者: Jones, Gerrad D.