NERC-NSFGEO SMARTWATER: Diagnosing controls of pollution hot spots and hot moments and their impact on catchment water quality

NERC-NSFGEO SMARTWATER：诊断污染热点和热点时刻的控制及其对流域水质的影响

• 批准号: NE/X018830/1
• 负责人: Stefan Krause
• 金额: $ 132.05万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX018830%2F1
• 关键词: NERC; NSFGEO; SMARTWATER; Diagnosing; controls

Planetary boundaries of river water pollution are at risk of being breached, with dangerous consequences for human and environmental health, economic prosperity, and water security. The current paradigm for environmental management is predicated on understanding of average conditions. However, we know environmental pollution can vary markedly in space and time. This interdisciplinary Large Grant (co-created with non-academic partners and as NERC-NSF collaboration) will pioneer innovations in experimental analytics, data science and mathematical modelling to yield new mechanistic understandings of the dynamic drivers of multi-contaminant pollution hotspots (spaces) and hot moments (times) in a changing water world. The diagnosis of the impact of these locations and periods when average pollution conditions are far exceeded on large scale and long-term river basin water quality is critical to inform local and global adaptation and mitigation strategies for river pollution and develop interventions to keep within a safe(r) 'operating space' and improve water quality for people and the environment. SMARTWATER will therefore integrate environmental sensing, network and data science innovations, and mathematical modelling with stakeholders' catchment knowledge to transform the way we diagnose, understand, predict, and manage water pollution hotspots and hot moments.We will:1. Pioneer the application of scalable field diagnostic technologies for water quality sensing and sampling for identifying and characterising multi-pollution hotspots and hot moments for emerging (e.g., wastewater indicators, pharmaceuticals, pesticides) and legacy (e.g., nutrients) contaminants.2. Develop smart water quality monitoring network solutions at river basin scale based on integrating high-resolution networks of proxy water pollution indicators with multivariate UAV boat-based longitudinal river network sampling to understand the footprint, propagation and persistence of pollution hotspots and hot moments in river basins.3. Develop and apply data science innovations integrating deep machine learning and artificial intelligence approaches for pollution source attribution and to identify how hotspots and hot moments of multi-pollutions dynamics results from pollution source activation, connectivity and river network transport and transformation.4. Demonstrate the utility of the new generation of smart pollution data to improve the capacity of integrated river basin scale water quality models to adequately present and predict the emergence of pollution hotspots and hot moments including their large-scale footprint and longer-term relevance for catchment water pollution.5. Co-create with our stakeholder community pathways for successfully implementing practical and policy relevant changes in water quality management practice and use the interdisciplinary and inter-sectoral expertise of our broad stakeholder base to inform knowledge generation and dissemination pipelines in SMARTWATER.The mechanistic process understanding and integrated technological and management solutions that will be developed in SMARTWATER will allow a step change in the diagnostics, prediction and management of water pollution and transform our ability to understand and tackle pollution pressures of increasing complexity in a rapidly changing environment.

河流水污染的地球边界有被突破的危险，对人类和环境健康、经济繁荣和水安全造成危险后果。目前的环境管理模式是建立在对平均条件的理解之上的。然而，我们知道环境污染在空间和时间上会有明显的变化。这个跨学科的大型赠款（与非学术合作伙伴共同创建，并作为NERC-NSF合作）将在实验分析，数据科学和数学建模方面开拓创新，以产生对多污染物污染热点（空间）的动态驱动因素的新的机械理解。诊断这些地点和时期的影响，当平均污染条件远远超过大规模和长期的河流流域水质是至关重要的，为当地和全球适应和缓解河流污染的战略，并制定干预措施，以保持在一个安全的（r）“操作空间”和改善水质的人和环境。因此，SMARTWATER将环境传感、网络和数据科学创新以及数学建模与利益相关者的流域知识相结合，以改变我们诊断、理解、预测和管理水污染热点和热点时刻的方式。率先将可扩展的现场诊断技术应用于水质传感和采样，以确定和表征多污染热点和热点时刻，废水指标、药品、农药）和遗留问题（例如，营养素）污染物。开发流域尺度的智能水质监测网络解决方案，将高分辨率水污染指标网络与多变量无人机船纵向河网采样相结合，了解流域污染热点和热点时刻的足迹、传播和持续性。3.开发和应用数据科学创新，将深度机器学习和人工智能方法集成到污染源归因中，并确定污染源激活、连通性和河流网络传输和转化如何导致多污染动态的热点和热点时刻。展示新一代智能污染数据的实用性，以提高综合河流流域尺度水质模型的能力，以充分展示和预测污染热点和热点时刻的出现，包括其大规模足迹和与流域水污染的长期相关性。与我们的利益相关者社区共同创建路径，以成功实施水质管理实践中的实际和政策相关变化，并利用我们广泛的利益相关者基础的跨学科和跨部门专业知识，为智能水务的知识生成和传播管道提供信息。智能水务将开发的机械过程理解和综合技术和管理解决方案将使诊断发生步骤性变化，预测和管理水污染，并转变我们的能力，以了解和解决日益复杂的污染压力，在迅速变化的环境。