Measuring pesticide exposure and microbial-level impacts in soil ecosystems using novel passive sampler devices

使用新型被动采样器设备测量土壤生态系统中农药暴露和微生物水平的影响

• 批准号: 2755355
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2755355
• 关键词: Measuring; pesticide; exposure; microbial; level

Microbes represent the lowest trophic level and entry point to the food web and are critical indicators of soil ecosystem health under chemical stress.This interdisciplinary project will combine our expertise in analytical chemistry, soil science, genomic sequencing, and machine learning to develop and validate a new technology to measure impacts in soil. Specifically, this will focus on the development of a passive sampling probe device (PSD) for soil. This probe will house physical, chemical, and microbial sampling functions to show how specific indicator taxa and the wider soil microbiome are affected upon exposure to (agri)chemicals.Key to this project is our unique capability to scale this study up and deploy a large number of PSD probes in controlled-condition soils. Using metagenomic sequencing data, machine learning will be used to identify indicator taxa that have been impacted by different chemical classes and evaluate their potential use as biosensors.Ultimately, this project will provide new, multifunctional and adaptable probes for soil microbiology and chemistry sampling, coupled with scalable supporting analytical methodologies to better understand chemical transport, fate and microbial impacts.Importantly, this project will provide a much-needed evidence base for the sustainable use of microbes as biosensors of chemical impacts in the terrestrial environment for the first time.

微生物代表着最低的营养水平和食物网的入口点，是化学胁迫下土壤生态系统健康的关键指标。这个跨学科的项目将联合收割机我们在分析化学，土壤科学，基因组测序和机器学习方面的专业知识，开发和验证一种新的技术来测量土壤中的影响。具体而言，这将集中在土壤的被动采样探头设备（PSD）的发展。该探测器将包含物理、化学和微生物采样功能，以显示特定指示类群和更广泛的土壤微生物组在暴露于（农业）化学品后如何受到影响。该项目的关键是我们独特的能力，可以扩大这项研究，并在受控条件下的土壤中部署大量PSD探测器。利用宏基因组测序数据，机器学习将用于识别受不同化学类别影响的指示类群，并评估其作为生物传感器的潜在用途。最终，该项目将为土壤微生物学和化学采样提供新的、多功能的和适应性强的探针，以及可扩展的支持分析方法，以更好地了解化学物质的迁移、归宿和微生物影响。该项目将首次为可持续利用微生物作为陆地环境中化学影响的生物传感器提供急需的证据基础。