Repairing the Leaks in Drinking Water Regulations
Bibliographic record
Abstract
The drinking water crisis in Flint, Michigan captured the attention of much of the nation and spurred responses from both state and federal regulators. Several months after the crisis, the U.S. Environmental Protection Agency (EPA) decided to consider making significant long-term revisions to its Lead and Copper Rule, which regulates—among other things—lead in drinking water. During a time of increased public scrutiny, these revisions raise the question of how regulators can improve regulations to prevent another drinking water crisis. Some scholars appear to have an answer. In a recent study, Adrienne Katner, an assistant professor of environmental and occupational health studies at Louisiana State University, and several coauthors report that current federal drinking water regulations are insufficient to prevent lead poisoning and environmental injustice in poor communities. According to Katner and her colleagues, the Lead and Copper Rule’s failure to incorporate a health-based standard into drinking water regulations hinders EPA’s efforts to protect public health. For this reason, they advocate for EPA to adopt a health-based standard—a type of regulation that sets a maximum permissible level for exposure to a dangerous substance—as part of its revisions to the Rule. Some federal environmental laws already impose health-based standards. One example is the Clean Air Act, which requires EPA to set health-based standards for hazardous air pollutants. On the other hand, the Safe Drinking Water Act, which gives EPA the authority to regulate drinking water, does not require a health-based standard and requires only that EPA set water contamination goals. Just like the Safe Drinking Water Act, the Lead and Copper Rule does not impose a health-based standard for lead concentration in drinking water and instead features a so-called “action level” for lead. An EPA action level acts as a threshold for the concentration of a dangerous substance, which, if exceeded, requires a water utility company to implement remedial measures. Remedial measures typically include increased monitoring and reporting requirements. However, Katner and colleagues point out that the action level is merely a screening tool. Its purpose is to assess whether water is corrosive and how well a utility’s corrosion control technology works. EPA set the current action level for lead at 15 parts per billion (ppb), which Katner and colleagues note was based on projections that a quarter of all water systems in the 1990s would exceed it, as well as on technological limitations existing in 1991 for lead detection in water. By contrast, the state of Michigan, the World Health Organization, and Canada have proposed a 10 ppb maximum as an appropriate level given modern technology and concerns about the sufficiency of current drinking water protections. Despite this, EPA has kept its action level at 15 ppb since the 1990s. This action level is insufficient to protect public health, Katner and colleagues argue, because even a lower level of 10 ppb may not be adequate to prevent fetuses, infants, and young children from experiencing adverse health impacts from lead exposure. Documented health effects from lead exposure include nervous system damage, learning disabilities, and impaired blood cell function. In addition, water utilities are not required to act on violations unless more than 10 percent of their tap water samples exceeds the action level. There are also no uniform requirements for the remedial actions utilities must take if enough samples exceed the limit. Instead, the mandated response differs according to the size and status of the utility, thus permitting significant variation in the steps taken to counteract lead detection in water supplies. To protect vulnerable populations from the negative impacts of lead exposure, Katner and colleagues recommend an enforceable health-based standard. Although they do not argue for a specific maximum permissible level of lead exposure, the authors mention that EPA currently has an unenforceable “goal” of 0 ppb, which could be a reasonable starting point for an eventual health-based standard. Moreover, Katner’s team suggests that a health-based standard of 0 ppb is prudent, since studies show that cumulative—that is, continuous, long-term—exposure to water lead levels as low as 1 ppb are associated with a 35 percent increase in young children’s blood lead levels. Ultimately, because exposure to just 1 ppb of lead may be harmful, Katner and colleagues insist that there is no known safe level of lead exposure. Even if an enforceable standard were set at 0 ppb, they argue that the Lead and Copper Rule’s 10 percent sampling threshold for requiring remedial action could subject people to dangerous levels of lead exposure, possibly even levels equivalent to those found in hazardous waste. Therefore, they contend that an enforceable health-based standard with a more stringent sampling threshold would improve current federal drinking water regulations by rectifying these deficiencies and ultimately achieve results that come as close as possible to ensuring adequate human protection. Despite vocal support for more stringent drinking water regulations, some scholars are more skeptical of incorporating health-based standards into environmental laws. For example, Professor Michael Livermore of the University of Virginia School of Law and Professor Richard Revesz of the New York University School of Law urge regulators to exercise caution when considering environmental standards that ignore the costs regulated entities must incur to comply with them. In the context of EPA’s air pollution regulations, for example, Livermore and Revesz believe that there is “no coherent, defensible” way that EPA can regulate based only on health considerations. Further, they point to empirical evidence showing that health-based standards often result in less stringent regulations than those derived from cost-benefit analyses, which force regulators to factor costs into their regulations. Although Livermore and Revesz did not discuss drinking water regulations in their paper, they suggest that their findings may be applicable to other regulatory efforts, even though the impact of a health-based standard for drinking water regulations remains uncertain. Nevertheless, as of May 2017, EPA is still considering a health-based benchmark, among several other options, to address what it considers to be “a compelling need to strengthen” drinking water protections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".