Patrick L. Gurian

Aluminium complexes of N,N′-ethylenebis(salicylideneimine)(H2salen). X-Ray crystal structures of [{Al(salen)}2(µ-O)]·MeCN and [Al(OC6H2Me3-2,4,6)(salen)]

The reaction of AlMe3 with H2salen [N,N′-ethylenenbis(salicylideneimine)] in MeCN gives the five-co-ordinate aluminium complex [AlMe(salen)]1. The interaction of 1 with H2O, HOPh, HOC6H4But-4, HOC6H2Me3-2,4,6, HO2CMe, HO2CPh, acetylacetone and 2-HOC6H4C(O)Me has been studied, and the X-ray structures of two of the products [{Al(salen)}2(µ-O)]·MeCN and [Al(OC6H2Me3-2,4,6)(salen)] have been determined. The proton and 27Al-{1H} NMR and IR spectra of the compounds have been recorded.

Arsenic exposure in US public and domestic drinking water supplies: a comparative risk assessment.

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.

Setting Risk‐Informed Environmental Standards for Bacillus Anthracis Spores

In many cases, human health risk from biological agents is associated with aerosol exposures. Because air concentrations decline rapidly after a release, it may be necessary to use concentrations found in other environmental media to infer future or past aerosol exposures. This article presents an approach for linking environmental concentrations of Bacillus. anthracis (B. anthracis) spores on walls, floors, ventilation system filters, and in human nasal passages with human health risk from exposure to B. anthracis spores. This approach is then used to calculate example values of risk-informed concentration standards for both retrospective risk mitigation (e.g., prophylactic antibiotics) and prospective risk mitigation (e.g., environmental clean up and reoccupancy). A large number of assumptions are required to calculate these values, and the resulting values have large uncertainties associated with them. The values calculated here suggest that documenting compliance with risks in the range of 10(-4) to 10(-6) would be challenging for small diameter (respirable) spore particles. For less stringent risk targets and for releases of larger diameter particles (which are less respirable and hence less hazardous), environmental sampling would be more promising.

Peri-urbanization and in-home environmental health risks: the side effects of planned and unplanned growth.

Urbanization is occurring at an explosive rate, and many cities, especially in low-income countries, are struggling to control growth and stop the development of unplanned communities. This research investigates the in-home environmental health risks associated with planned and unplanned growth in peri-urban communities (neighborhoods) of a rapidly growing city in Northern Mexico. Surveys were conducted during home visits to 98 households in a planned community and 202 households in unplanned communities of comparable, though slightly higher, socio-economic status. To assess the differences for the two types of communities, multiple risk factors were measured and compared. Results showed that households in both planned and unplanned communities were vulnerable to poor environmental health conditions, but more risk factors and negative health outcomes were associated with unplanned communities. Some of the obvious benefits of living in a planned community included better-constructed homes and water and sewer connections. Other study results indicated that households in the planned community reported fewer cases of respiratory problems (P = 0.039) and dizziness (P = 0.009). They were also less likely to store insecticides and other chemicals inappropriately in their home (P < 0.001). Providing adequate housing infrastructure appears to provide a number of important benefits to health and behavior beyond the simple provision of additional amenities in the home.

Risk assessment strategies as nanomaterials transition into commercial applications

Commercial applications of nanomaterials are rapidly emerging in the marketplace. The environmental and human health risks of many nanomaterials remain unknown, and prioritizing how to efficiently assess their risks is essential. As nanomaterials are incorporated into a broader range of commercial products, their potential for environmental release and human exposure not only increases, but also becomes more difficult to model accurately. Emphasis may first be placed on estimating potential environmental exposure based on pertinent physical properties of the nanomaterials. Given that the greatest potential for global environmental impacts results from nanomaterials that are both persistent and toxic, this paper advocates screening first for persistence since it is easier to assess than toxicity. For materials that show potential for persistence, a higher burden of proof of their non-toxicity is suggested before they enter the commercial marketplace whereas a lower burden of proof may be acceptable for nanomaterials that are less persistent.

Cryptosporidium and Giardia in tropical recreational marine waters contaminated with domestic sewage: Estimation of bathing-associated disease risks

Sewage is a major contributor to pollution problems involving human pathogens in tropical coastal areas. This study investigated the occurrence of intestinal protozoan parasites (Giardia and Cryptosporidium) in tropical recreational marine waters contaminated with sewage. The potential risks of Cryptosporidium and Giardia infection from recreational water exposure were estimated from the levels of viable (oo) cysts (DIC+, DAPI+, PI-) found in near-shore swimming areas using an exponential dose response model. A Monte Carlo uncertainty analysis was performed in order to determine the probability distribution of risks. Microbial indicators of recreational water quality (enterococci, Clostridium perfringens) and genetic markers of sewage pollution (human-specific Bacteroidales marker [HF183] and Clostridium coccoides) were simultaneously evaluated in order to estimate the extent of water quality deterioration associated with human wastes. The study revealed the potential risk of parasite infections via primary contact with tropical marine waters contaminated with sewage; higher risk estimates for Giardia than for Cryptosporidium were found. Mean risks estimated by Monte Carlo were below the U.S. EPA upper bound on recreational risk of 0.036 for cryptosporidiosis and giardiasis for both children and adults. However, 95th percentile estimates for giardiasis for children exceeded the 0.036 level. Environmental surveillance of microbial pathogens is crucial in order to control and eradicate the effects that increasing anthropogenic impacts have on marine ecosystems and human health.

Benefit‐cost estimation for alternative drinking water maximum contaminant levels

A simulation model for estimating compliance behavior and resulting costs at U.S. Community Water Suppliers is developed and applied to the evaluation of a more stringent maximum contaminant level (MCL) for arsenic. Probability distributions of source water arsenic concentrations are simulated using a statistical model conditioned on system location (state) and source water type (surface water or groundwater). This model is fit to two recent national surveys of source waters, then applied with the model explanatory variables for the population of U.S. Community Water Suppliers. Existing treatment types and arsenic removal efficiencies are also simulated. Utilities with finished water arsenic concentrations above the proposed MCL are assumed to select the least cost option compatible with their existing treatment from among 21 available compliance strategies and processes for meeting the standard. Estimated costs and arsenic exposure reductions at individual suppliers are aggregated to estimate the national compliance cost, arsenic exposure reduction, and resulting bladder cancer risk reduction. Uncertainties in the estimates are characterized based on uncertainties in the occurrence model parameters, existing treatment types, treatment removal efficiencies, costs, and the bladder cancer dose-response function for arsenic.

General Methodology Combining Engineering Optimization of Primary HVAC&R Plants with Decision Analysis Methods—Part II: Uncertainty and Decision Analysis

A companion paper (Jiang and Reddy 2007) presents a general and computationally efficient methodology for dynamic scheduling and optimal control of complex primary HVAC&R plants using a deterministic engineering optimization approach. The objective of this paper is to complement the previous work by proposing a methodology by which the robustness of the optimal deterministic strategy to various sources of uncertainties can be evaluated against non-optimal but risk averse alternatives within a formal decision analysis framework. This specifically involves performing a sensitivity analysis on the effect of various stochastic factors that impact primary HVAC&R plant optimization, such as the uncertainty in load prediction and the uncertainties associated with various component models of the equipment. This is achieved through Monte Carlo simulations on the deterministic outcome, which allow additional attributes, such as its variability and the probability of insufficient cooling, to be determined along with the minimum operating cost. The entire analysis is then repeated for a specific non-optimal but risk-averse operating strategy. Finally, a formal decision analysis model using linear multi-attribute utility functions is suggested for comparing both these strategies in a framework that explicitly models the risk perception of the plant operator in terms of the three attributes. The methodology is demonstrated using the same illustrative case study as the companion paper.

Relating occupant perceived control and thermal comfort: Statistical analysis on the ASHRAE RP-884 database

Recent literature has suggested that a building occupants level of perceived control over their thermal environment plays a significant role in determining their thermal comfort response. The ASHRAE RP-884 report, which led to an optional Adaptive Comfort Standard for use in naturally ventilated office buildings, was a key attempt to identify relationships between perceived control levels and comfort across a large database of occupant responses in the field. Though the study found no conclusive evidence of these relationships, the authors acknowledged that their analytical process involved many questionable assumptions and generalizations, which may have compromised their findings. This study improves upon the RP-884 analysis by taking a more detailed look at three HVAC building studies from within the RP-884 database. Each of these studies contains direct occupant ratings of both perceived control and thermal comfort—ratings that were not available for many of the other RP-884 buildings. Statistical analysis shows that given this detailed focus, significant correlations do indeed appear between key comfort and perceived control variables within the datasets.

Analysis of Contaminant Co-Occurrence in Community Water Systems

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.