Downstream Effects of Upstream Causes

The United States Environmental Protection Agency considers nutrient pollution in stream ecosystems one of the United States’ most pressing environmental challenges. But limited independent replicates, lack of experimental randomization, and space- and time-varying confounding handicap causal inference on effects of nutrient pollution. In this article, the causal <i>g</i>-methods are extended to allow for exposures to vary in time and space in order to assess the effects of nutrient pollution on chlorophyll <i>a</i>—a proxy for algal production. Publicly available data from North Carolina’s Cape Fear River and a simulation study are used to show how causal effects of upstream nutrient concentrations on downstream chlorophyll <i>a</i> levels may be estimated from typical water quality monitoring data. Estimates obtained from the parametric <i>g</i>-formula, a marginal structural model, and a structural nested model indicate that chlorophyll <i>a</i> concentrations at Lock and Dam 1 were influenced by nitrate concentrations measured 86 to 109 km upstream, an area where four major industrial and municipal point sources discharge wastewater. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

美国环境保护署（United States Environmental Protection Agency）将河流生态系统中的营养物污染列为美国最紧迫的环境挑战之一。然而，有限的独立重复样本、实验随机化的缺失，以及随空间与时间变化的混杂因素，极大阻碍了针对营养物污染影响的因果推断。本文针对因果<i>g</i>方法进行拓展，以允许暴露因素在时空维度上发生变化，借此评估营养物污染对叶绿素<i>a</i>（chlorophyll a，藻类生产力的替代指标）的影响。研究采用北卡罗来纳州卡皮费尔河的公开水质监测数据与一项模拟研究，演示了如何从常规水质监测数据中估算上游营养盐浓度对下游叶绿素<i>a</i>水平的因果效应。基于参数化<i>g</i>公式、边际结构模型与结构嵌套模型得到的估计结果显示，1号船闸大坝处的叶绿素<i>a</i>浓度会受到上游86至109公里处的硝酸盐浓度影响，该区域分布有四大主要工业与市政点源排放废水。本文的补充材料（包含可复现本研究的标准化材料说明）可作为在线补充资料获取。