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面临着被突破的风险，对人类和环境健康、经济繁荣和水安全造成危险后果。当前的环境管理范式基于对平均条件的理解。然而，我们知道环境污染在空间和时间上可能存在显着差异。这项跨学科的大笔资助（与非学术合作伙伴共同创建，并作为 NERC-NSF 合作）将引领实验分析、数据科学和数学建模方面的创新，以对多污染物污染热点（空间）的动态驱动因素产生新的机制理解以及不断变化的水世界中的热门时刻（时代）。对大规模和长期流域水质远超平均污染条件的这些地点和时期的影响进行诊断对于制定当地和全球河流污染适应和缓解战略以及制定干预措施以将河流污染保持在安全范围内至关重要。 (r)“操作空间”并改善人类和环境的水质。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。我们将：1．率先应用可扩展的现场诊断技术进行水质传感和采样，以识别和表征新兴污染物（例如废水指标、药品、农药）和遗留污染物（例如营养物）的多重污染热点和热点时刻2。基于代理水污染指标的高分辨率网络与多元无人机船基纵向河网采样相结合，开发流域尺度的智能水质监测网络解决方案，以了解流域污染热点和热点时刻的足迹、传播和持续性.3.开发和应用集成深度机器学习和人工智能方法的数据科学创新，用于污染源归因，并确定污染源激活、连通性和河网传输和转化如何导致多种污染动态的热点和热点时刻。 4．展示新一代智能污染数据的实用性，以提高综合流域规模水质模型的能力，以充分呈现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的长期相关性污染.5.与我们的利益相关者社区共同创建成功实施水质管理实践中实际和政策相关变革的途径，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 SMARTWATER 中的知识生成和传播渠道提供信息。 SMARTWATER 中开发的集成技术和管理解决方案将使水污染的诊断、预测和管理发生重大变化，并转变我们理解和应对快速变化的环境中日益复杂的污染压力的能力。