Human Exposure and Vulnerability to Manganese Contaminated Groundwater

项目摘要/摘要 锰是一种天然存在的金属污染物，存在于世界各地的供水系统中。 尽管使用来自孟加拉国、中国和加拿大的小样本(400个观察值)进行的研究表明 过量摄入锰对婴儿和幼儿有神经毒性，美国 美国环保局(US EPA)仅将锰列为二次污染物。因此，锰不是 在公共供水系统中接受强制性监测，更不用说为200多万口供水的私人水井了 加州人仍然不受监管，不受治疗。为了调和流行病学之间的差距 证据和监管行动，我们提出这一及时的研究。我们的项目，将加州的 私人油井作为其调查的焦点，有三个具体目标：(1)查明(1)高风险地区 饮用水中锰暴露(2)锰高危社区的社会经济特征 以及(3)锰暴露与婴儿和儿童健康结局之间的关联程度 年幼的孩子。虽然处理受锰污染的水在技术上很简单，但Access可以 取决于一个社区的社会经济和政治地位；因此，存在着MN悄悄放大的危险 健康不平等。这项研究的​更广泛的长期目标是：(I)鼓励进一步分析 通过从地下水锰数据中开发全州公开可用的数据集， 以及(Ii)为研究锰对婴儿和儿童健康的影响建立一个初级样本研究的因果分析框架(​Causse​)。 加利福尼亚州执行美国环保局的二级污染物标准50微克L​-1，但仅限于社区水 系统。非瞬变、非社区供水系统(如学校和医院)和私人水井 免税。近年来，美国地质调查局(USGS)发现锰浓度超过 加州油井中基于健康的目标是300ug L​-1。​建议的研究将表明是否和要 锰对儿童健康的影响程度以及造成健康差距的程度。因此，它与 NIEHS的使命是确定环境暴露如何影响人类，以促进更健康 活着。为了达到确定暴露在不安全浓度的锰的高风险地区的第一个目标，我们 将使用关于地下水流和地下水化学的机器学习模型来预测锰 集中在中央山谷、洛杉矶、中央海岸和西海岸盆地；然后，我们将 使用邮政编码和学区面叠加MN预测格网以确定出现的区域 有超过50%的机会接触二级标准的锰为高风险。对于第二个目的， 我们将确定高风险地区与低风险地区是否根据其社会经济地位和 使用双样本t检验进行人口统计学分析。对于第三个目标，我们将运行出生的最小二乘回归 体重、各年级的考试成绩和长期缺勤率与对照组预测的锰暴露 化学混杂、社会经济地位和人口统计特征。

Project Summary / Abstract Manganese (Mn) is a naturally-occurring metal contaminant found in water supplies throughout the world. Although research using small samples (< 400 observations) from Bangladesh, China and Canada suggests that excessive consumption of Mn is neurotoxic in infants and young children, the United States Environmental Protection Agency (US EPA) classifies Mn only as a secondary contaminant. Thus, Mn is not subject to mandatory monitoring in public water systems, let alone private wells, which serve over 2 million Californians while remaining unregulated and untreated. To reconcile the gap between epidemiological evidence and regulatory action, we propose this timely research. Our project, which takes California’s private wells as a focal point for its investigation, has three specific aims: to identify (1) areas at high risk of exposure to Mn in drinking water (2) the socioeconomic characteristics of communities at high-risk of Mn exposure, and (3) the degree of association between Mn exposure and the health outcomes of infants and young children. While treatment for Mn-contaminated water is technologically straightforward, access may depend on a community’s socioeconomic and political status; thus, there is danger of Mn silently amplifying health inequalities. The ​broader, long-term objectives of this research are (I) to encourage further analyses of Mn exposure in California by developing a statewide publicly-available dataset from groundwater Mn data, and (II) to develop a ​causal ​framework for a primary-sample study of Mn effects on infant and child health. California enforces the US EPA’s secondary contaminant standard of 50 µg L​-1 but only in community water systems. Non-transient, non-community water systems (e.g. schools and hospitals) and private wells are exempt. In recent years, the US Geological Survey (USGS) has found Mn concentrations above the health-based target of 300 µg L​-1 in California’s wells. ​The proposed research will indicate whether and to what extent Mn affects child health and contributes to health disparities. As such, it is aligned with the NIEHS mission to determine how environmental exposures affect humans in order to promote healthier lives. To achieve the first aim of identifying areas at high risk of exposure to unsafe concentrations of Mn, we will use a machine learning model on subsurface flow and groundwater chemistry to predict Mn concentrations in the Central Valley, Los Angeles, Central Coast and West Coast Basins; then, we will overlay the Mn prediction grid with ZIP code and school district polygons to identify the areas that emerge with more than a 50% chance of exposure to Mn at the secondary standard as high-risk. For the second aim, we will determine if high-risk areas are distinguished from low-risk areas by their socioeconomic status and demographics using two-sample t-tests. For the third aim, we will run least squares regressions of birth weight, grade-by-school test scores and chronic absenteeism rates on predicted Mn exposure with controls for chemical confounds, socioeconomic status and demographic characteristics.