Human Exposure and Vulnerability to Manganese Contaminated Groundwater

项目总结/摘要 锰（Mn）是一种天然存在的金属污染物，存在于世界各地的供水中。 尽管使用来自孟加拉国、中国和加拿大的小样本（< 400个观测值）的研究表明， 过量摄入锰对婴幼儿有神经毒性， 美国环境保护署（EPA）仅将Mn归类为二次污染物。因此，Mn不是 公共供水系统必须接受强制监测，更不用说为超过200万人供水的私人威尔斯了 加州人，而仍然不受管制和治疗。为了调和流行病学与非流行病学之间的差距， 证据和监管行动，我们建议及时研究。我们的项目，将加州的 作为其调查重点的私人威尔斯，有三个具体目标：查明（1） 饮用水中锰暴露（2）锰高风险社区的社会经济特征 暴露，以及（3）锰暴露与婴儿健康结果之间的关联程度， 年幼的孩子。虽然锰污染水的处理在技术上是简单的， 取决于一个社区的社会经济和政治地位;因此，存在Mn无声放大的危险 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范围内公开可用的数据集， 和（II）制定一个因果关系框架的初级样本研究锰对婴幼儿健康的影响。 加州执行美国环保署的二级污染物标准50 µg L-1，但仅适用于社区用水 系统.非临时性、非社区供水系统（如学校和医院）和私人威尔斯井， 豁免。近年来，美国地质调查局（USGS）发现， 加州威尔斯井中基于健康的目标为300 µg L-1。 拟议的研究将表明， 锰在多大程度上影响儿童健康并造成健康差异。因此，它与 NIEHS的使命是确定环境暴露如何影响人类，以促进健康 生活为了实现确定暴露于不安全浓度锰的高风险区域的首要目标，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Mn 浓度在中央谷，洛杉矶，中央海岸和西海岸盆地;然后，我们将 用邮政编码和学区面覆盖Mn预测格网，以确定出现的区域 有超过50%的机会暴露于二级标准的锰为高风险。对于第二个目标， 我们将确定高风险地区是否根据其社会经济地位与低风险地区区分开来， 使用双样本t检验的人口统计学。对于第三个目标，我们将运行出生的最小二乘回归 体重、各年级学校考试成绩和慢性缺勤率对预测的锰暴露的影响 化学成分社会经济地位和人口特征

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.