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Repairing the Leaks in Drinking Water Regulations

2017· article· en· W7024335045 sur OpenAlexaboutno aff

Notice bibliographique

RevueeYLS (Yale Law School) · 2017
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueCold Atom Physics and Bose-Einstein Condensates
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSafe Drinking Water ActHazardous wasteMaximum Contaminant LevelClean Water ActClean Air ActAgency (philosophy)InjusticeHazardous substanceWater supply
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,015
score de la tête « metaresearch » (Gemma)0,052
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,036
Score d'incertitude au seuil0,081

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0150,052
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0080,012
Communication savante0,0100,018
Science ouverte0,0020,007
Intégrité de la recherche0,0110,015
Charge utile insuffisante (le modèle a refusé de juger)0,0110,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,016
Tête enseignante GPT0,252
Écart entre enseignants0,236 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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