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.

河水污染的行星边界有被破坏的风险，对人类和环境健康，经济繁荣和水安全造成了危险的后果。当前用于环境管理的范式取决于对平均条件的理解。但是，我们知道环境污染在时空和时间上可能有明显变化。这项跨学科的大笔赠款（与非学术伙伴共同创建，作为NERC-NSF协作）将在实验分析，数据科学和数学建模中开创创新，以产生对多抗激素污染热点热点（Space）和热点（及时的（及时）（及时的污水世界）的动态驱动因素的新机械理解。在大规模和长期河流盆地水质远远超过平均污染条件时，诊断这些位置和期间的影响对于为河流污染的本地和全球适应性和缓解策略提供了至关重要的，并制定了干预措施，以保持安全（R）“操作空间”并改善人们和环境的水质。因此，Smartwater将与利益相关者的集水学知识相结合，将环境感应，网络和数据科学创新以及数学建模进行整合，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。先驱者将可扩展的现场诊断技术应用于水质感测和采样，以识别和表征多污染热点以及用于新兴（例如废水指示剂，药品，农药）和遗产（例如，营养物质）污染物的热点（例如，废水指标，药物，农药）。基于将代理水污染指标与多元无人机基于无人机的纵向河流网络采样的高分辨率网络相结合，以了解河流流域量表的智能水质监测网络解决方案，以了解河流中污染热点和炎热时刻的占地面积，繁殖和持久性。3。开发和应用数据科学创新，整合了深度机器学习和人工智能方法，以确定污染源激活，连通性和河流网络运输和转换的热点和热点动态的热点和热点。4。展示新一代智能污染数据的实用性，以提高综合河流盆地量表水质模型的能力，以充分呈现和预测污染热点和热点的出现，包括它们的大规模足迹和与流域水污染的长期相关性5。与我们的利益相关者社区的途径共同创造，以成功地实施水质管理实践中的实用和政策相关的变化，并使用我们广泛的利益相关者基础的跨学科和部门专业知识，以在智能水上为知识的产生和传播管道提供信息，以了解机械过程的理解以及允许我们允许智能及诊断及其诊断的既定能力和管理的解决方案应对在迅速变化的环境中增加复杂性的污染压力。