Developing a real-time early warning system for potable water sources

开发饮用水源实时预警系统

• 批准号: 567513-2021
• 负责人: Peng, Hui
• 金额: $ 3.28万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=723507
• 关键词: Developing; real; time; early; warning

Canada's freshwater ecosystem and drinking water quality has been negatively impacted by harmful algal blooms (HABs). HABs can produce high concentrations of cyanotoxins and natural organic matters which pose a severe threat to Canadian health. However, current water quality monitoring systems are laborious to characterize cyanotoxins or natural organic matters. By teaming up with academic, government and industry partners, we herein proposed to develop an early warning system to forecast the concentrations of cyanotoxins and natural organic matters in source drinking waters, by integrating a field-deployable multi-parameter sensor and machine learning algorithms. The machine learning algorithms will be benchmarked by characterizing grab samples with high-resolution mass spectrometry based nontargeted analysis. A total of 1,728 grab sample data points will be produced across 12 months. The dataset will be used to validate the machine-learning algorithms by matching to real-time UV-Vis absorbance spectra collected by remote sensors. The early warning system established in the current 1-year project will help to forecast source water quality and protect Canadian health from drinking water pollution.

加拿大的淡水生态系统和饮用水质量受到有害藻华（HABs）的负面影响。有害藻华可产生高浓度的蓝藻毒素和天然有机物，对加拿大人的健康构成严重威胁。然而，目前的水质监测系统是费力表征蓝藻毒素或天然有机物质。通过与学术界、政府和行业合作伙伴合作，我们建议开发一个预警系统，通过集成可现场部署的多参数传感器和机器学习算法，预测水源饮用水中蓝藻毒素和天然有机物的浓度。机器学习算法将通过基于非靶向分析的高分辨率质谱表征抓取样本来进行基准测试。总共1728个抓取样本数据点将在12个月内产生。该数据集将用于通过匹配远程传感器收集的实时UV-Vis吸收光谱来验证机器学习算法。在目前为期1年的项目中建立的预警系统将有助于预测水源水质，保护加拿大人的健康免受饮用水污染。