High Resolution Models of Groundwater Metal Exposures

地下水金属暴露的高分辨率模型

• 批准号: 10354269
• 负责人: Steven N. Chillrud
• 金额: $ 30.41万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10354269
• 关键词: Address; Affect; Area; Arsenic; Cardiometabolic Disease; Chemicals; Cheyenne; Cohort Studies; Communities; Dangerousness; Data; Data Analyses; Data Collection; Data Set; Development; Environmental Exposure; Environmental Pollution; Environmental Risk Factor; Epidemic; Evaluation; Exposure to; Filtration; Foundations; Future; Goals; Health; Heart; Household; Individual; Intervention; Isotopes; Length; Location; Measurement; Measures; Metal exposure; Metals; Methods; Modeling; Native Americans; Nitrates; Oxidation-Reduction; Participant; Pathway interactions; Policies; Population; Populations at Risk; Privatization; Probability; Process; Publications; Recording of previous events; Research; Resolution; Resources; Rivers; Role; Running; Rural Community; Sioux Indians; Site; Source; South Dakota; Southeastern Asia; Spatial Distribution; Superfund; Surface; Surveys; System; Testing; Time trend; Toxicology; Tribes; Universities; Uranium; Water; Water Supply; anthropogenesis; base; cohort; community engagement; contaminated drinking water; cooking; density; drinking; drinking water; effective intervention; geochemistry; ground water; hazard; hydrology; implementation intervention; improved; interest; machine learning model; machine learning prediction; member; meter; northern Native American; northern plains; oxidation; predictive modeling; programs; remediation; remote sensing; rural area; therapy development; trend; tribal community; urinary; water quality

Project 1 Summary The long-running Strong Heart Study documented high urinary arsenic (As) and uranium (U) levels in Native American tribal study sites relative to the general US population. Elevated exposures to metals and metalloids (hereafter metals) potentially contribute to the epidemic of cardiometabolic disease affecting tribal and other rural communities in the US. In rural communities of the Northern Plains, drinking water is commonly derived from unregulated and largely unmonitored private wells or public water systems that draw from the same aquifers. Much of the As and U exposure likely results from elevated levels of metals in groundwater sources. However, groundwater data are relatively scarce for rural communities, especially from tribal regions, making it difficult to identify groundwater hazards, and to mitigate their exposures. Project 1 of the Columbia University Northern Plains Superfund Research Program (CUNP-SRP) uses a two-pronged approach to address a critical gap in our understanding of the distribution of groundwater contamination and exposure across rural areas including Native American tribal areas. First, we will integrate existing and new measurements of groundwater composition data with prior large mineralogical and chemical speciation data sets. Second, we will use a combination of groundwater composition in wells and remotely sensed hydrological and other variables to develop machine learning-based predictive models of groundwater contaminant levels at the household scale—the foundational data needed for exposure assessment. We have 3 specific aims. Aim 1 will analyze drinking water sources from the CUNP-SRP field areas to identify and better understand the mobilization mechanisms of As and U into drinking water. Aim 2 will develop process-based models that predict As and U drinking water concentrations at the household scale across the Northern Plains. Aim 3 will develop and validate As and U drinking water exposure profiles for the Strong Heart Arsenic and Uranium Lifelong (SHAUL) cohort (for Project 3) by comparing spatial and temporal variability and trends in water metal concentrations, water use, and detailed residential histories with urinary-based exposures for tribal participants from all SHAUL locations. We will also evaluate the impact of policy interventions on drinking water As and U exposures, including those from changes in water sources and use, implementation of As MCL on contaminant concentrations in CWSs, and implementation of point-of-use filtration. Combined, our data and models will provide improved understanding of environmental exposures that is needed for the CUNP-SRP to contextualize the health effects of hazardous metal mixtures (Projects 3 and 4), to identify mitigation pathways to clean water (Project 5), and to allow the Community Engagement and Administrative Cores to make the environmental data and their interpretations accessible to these tribal communities. Ultimately, the results of these efforts in data collection, modeling, and additional data analysis will help identify at-risk populations and develop effective interventions to reduce potentially dangerous exposures to contaminated drinking water in the Northern Plains and beyond.

项目1概要 长期运行的强心脏研究记录了本地人尿砷 (As) 和铀 (U) 水平较高 美国部落研究地点相对于美国普通人口的金属和类金属暴露量较高。 （以下称金属）可能会导致影响部落和其他农村地区的心脏代谢疾病的流行 在美国北部平原的农村社区，饮用水通常来自于。 来自同一含水层的不受监管且基本上不受监控的私人水井或公共供水系统。 大部分砷和铀暴露可能是由于地下水源中金属含量升高造成的。 对于农村社区，尤其是部落地区，地下水数据相对匮乏，因此很难 识别地下水危害，并减轻其暴露程度 哥伦比亚大学北方分校项目 1。 平原超级基金研究计划（CUNP-SRP）采用双管齐下的方法来解决我们的关键差距 了解农村地区（包括原住民）地下水污染和暴露情况的分布 首先，我们将整合现有和新的地下水成分测量数据。 其次，我们将结合使用先前的大型矿物学和化学形态数据集。 井中地下水成分以及遥感水文和其他变量以开发机器 基于学习的家庭规模地下水污染物水平预测模型——基础 暴露评估所需的数据有 3 个具体目标：分析饮用水源。 CUNP-SRP 实地领域，以确定并更好地了解 AS 和 U 的动员机制 目标 2 将开发基于过程的模型，预测饮用水中砷和铀的浓度。 目标 3 将开发和验证 As 和 U 饮用水。 通过比较强心脏砷和铀终身 (SHAUL) 队列（项目 3）的暴露概况 水金属浓度、用水和详细住宅的时空变化和趋势 我们还将评估来自所有 SHAUL 地点的部落参与者的尿液暴露史。 政策干预对饮用水砷和铀暴露的影响，包括水变化造成的影响 As MCL 对 CWS 污染物浓度的来源和使用，以及实施 结合使用点过滤，我们的数据和模型将加深对环境的了解。 CUNP-SRP 需要了解危险金属混合物对健康的影响 （项目 3 和 4），确定清洁水的缓解途径（项目 5），并允许社区 参与和管理核心，使环境数据及其解释可供访问 最终，这些部落社区在数据收集、建模和附加数据方面的努力的结果。 分析将有助于识别高危人群并制定有效的干预措施以减少潜在危险 北部平原及其他地区接触受污染的饮用水。