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Examines the revision of arsenic drinking water standard in the United States. Consideration of cost in promulgating maximum contaminant level for water; Discrepancy in the cost estimates for arsenic treatment; Importance of regulatory guidance by the state.

Although approximately 35 million people in the US obtain drinking water from domestic wells, few studies have investigated the risk of arsenic exposure from this source. In this paper arsenic concentrations were modeled for public and domestic wells using a dataset from the US Geological Survey (USGS). Excess lifetime and annual risks for lung and bladder cancer were calculated based on the carcinogenic potency and average arsenic concentrations in public and domestic water supplies. Monte Carlo uncertainty analysis was used to estimate the degree of confidence in these estimations. Results indicated that domestic well users accounted for 12% of the US population, but 23% of overall arsenic exposure from drinking water. Assuming that the new and more restrictive arsenic maximum contaminant limit (MCL) is implemented for public water supplies, it is anticipated that the proportion of people experiencing excess annual fatalities from drinking water from domestic wells will increase to 29% unless corresponding efforts are made to reduce exposures among domestic well users. Differences between public and domestic wells were not consistent across the nation. Public wells tend to tap deeper aquifers than domestic wells, and as a result local arsenic-depth trends can contribute to differences between public and domestic wells. Domestic wells and public wells in the western US have the highest arsenic levels with excess fatality risks estimated to be in the range of 1 per 9300 to 1 per 6600 in these regions. Uncertainty distributions of excess fatalities were developed and resultant uncertainties were propagated in arsenic exposure and potency factor. Uncertainty in the carcinogenic potency of arsenic was the dominant source of uncertainty in most regions, but for domestic wells in the New England and Southeast regions uncertainty in arsenic exposure was dominant, indicating that additional data on arsenic concentrations in these areas would substantially improve regional risk estimates. [ABSTRACT FROM AUTHOR]

The current framework for U.S. Environmental Protection Agency regulation of water quality in community drinking water supplies produces sequential rules for either single contaminants or small groups of similar contaminants. For both substantive and pragmatic reasons, some water industry experts have advocated the development of a more holistic regulatory process in which rules are promulgated less frequently but for larger contaminant classes. Such a framework would require the expansion of existing regulatory evaluation technologies to account for joint occurrence distributions of multiple contaminants. This article presents an analysis, using two national contaminant databases, of the joint distributions of seven contaminants (arsenic, nitrate, uranium, manganese, magnesium, calcium, and sulfate) in community water system source waters. Inferences are based on a flexible Bayesian hierarchical modeling structure with numerous features desirable for empirical exploration of multicontaminant regulations, including the simultaneous estimation of spatial heterogeneity in contaminant levels and covariations among contaminants, applicability to sparse data collected over a large spatial scale, and coherent assimilation of information provided by censored observations. The model is used to estimate a family of joint distributions for the contaminants indexed by water system characteristics, with empirically appropriate complexity given the resolution of the available data. The resulting distributions provide insights about the nature of, and uncertainty about, contaminant co-occurrence patterns, quantify the impact on national assessments of jointly modeling the contaminants, and facilitate identification of critical classes of water systems where uncertainty is highest. [ABSTRACT FROM AUTHOR]

Focuses on the correlation between diabetes prevalence and air pollution emissions in the U.S. Use of multivariate regression approach in the study; Analysis of the state level emissions data; Utilization of the data to improve health outcomes.

Observations of traffic inspections at a U.S. land border port of entry in El Paso, TX indicate that the process is highly variable. In a series of 24 half-hour observation periods of ordinary noncommercial traffic, the average inspection duration ranged from 16.6 s to 56.6 s. The proportion of inspections which involved some physical search of the vehicle, as indicated by the inspector leaving the inspection booth, varied from 5% to 56% in different observation periods. In 4 out of 10 cases, the log-mean of inspection duration in simultaneous observations of parallel lanes of traffic differed significantly (p<0.05). This suggests that differences in inspector behavior are responsible for much of the variability of the inspection process. Similar results are found for the SENTRI program. A survey of public perception reveals that a majority of English-language respondents perceive the inspectors to be fair while a majority of Spanish language respondents (both U.S. and Mexican citizens) perceive the process to be more arbitrary, as they state that fairness "depends on the inspector." Spanish-language respondents are also more likely to report having to submit to additional searches than English-language respondents. A common theme that emerges from the analysis of these two datasets is that efforts to standardize some aspects of inspections, while preserving inspector autonomy, may improve the performance of the process by eliminating variability which organized criminal groups may be able to exploit. [ABSTRACT FROM AUTHOR]