Relationships between polybrominated diphenyl ether concentrations in house dust and serum.

Polybrominated diphenyl ethers (PBDEs) have been measured in the home environment and in humans, but studies linking environmental levels to body burdens are limited. This study examines the relationship between PBDE concentrations in house dust and serum from adults residing in these homes. We measured PBDE concentrations in house dust from 50 homes and in serum of male-female couples from 12 of the homes. Detection rates, dust-serum, and within-matrix correlations varied by PBDE congener. There was a strong correlation (r = 0.65-0.89, p < 0.05) between dust and serum concentrations of several predominant PBDE congeners (BDE 47, 99, and 100). Dust and serum levels of BDE 153 were not correlated (r < 0.01). The correlation of dust and serum levels of BDE 209 could not be evaluated due to low detection rates of BDE 209 in serum. Serum concentrations of the sum of BDE 47, 99, and 100 were also strongly correlated within couples (r = 0.85, p = 0.0005). This study provides evidence that house dust is a primary exposure pathway of PBDEs and supports the use of dust PBDE concentrations as a marker for exposure to PBDE congeners other than BDE 153.

Dimensions, issues, and bills: Appropriations voting on the house floor

One of the fundamental findings in the congressional literature is that one or sometimes two dimensions can successfully describe roll-call voting. In this paper we investigate if we can reach the same conclusions about low dimensionality when we divide the roll-call agenda into subsets of relatively homogeneous subject matter. We are primarily interested in the degree to which the same ordering of representatives is yielded across these different groups of votes. To conduct our analysis we focus on all roll calls on the 13 annual appropriations bills across eight congresses. When we concentrate on these smaller issue areas, we find that voting is multidimensional and members do not vote in a consistent ideological fashion across all issue areas. Copyright © Southern Political Science Association 2010.

A COMPARISON OF MARKET CLEARING TOOLS IN ELECTRICITY SYSTEMS

To maintain a strict balance between demand and supply in the US power systems, the Independent System Operators (ISOs) schedule power plants and determine electricity prices using a market clearing model. This model determines for each time period and power plant, the times of startup, shutdown, the amount of power production, and the provisioning of spinning and non-spinning power generation reserves, etc. Such a deterministic optimization model takes as input the characteristics of all the generating units such as their power generation installed capacity, ramp rates, minimum up and down time requirements, and marginal costs for production, as well as the forecast of intermittent energy such as wind and solar, along with the minimum reserve requirement of the whole system. This reserve requirement is determined based on the likelihood of outages on the supply side and on the levels of error forecasts in demand and intermittent generation. With increased installed capacity of intermittent renewable energy, determining the appropriate level of reserve requirements has become harder. Stochastic market clearing models have been proposed as an alternative to deterministic market clearing models. Rather than using a fixed reserve targets as an input, stochastic market clearing models take different scenarios of wind power into consideration and determine reserves schedule as output. Using a scaled version of the power generation system of PJM, a regional transmission organization (RTO) that coordinates the movement of wholesale electricity in all or parts of 13 states and the District of Columbia, and wind scenarios generated from BPA (Bonneville Power Administration) data, this paper explores a comparison of the performance between a stochastic and deterministic model in market clearing. The two models are compared in their ability to contribute to the affordability, reliability and sustainability of the electricity system, measured in terms of total operational costs, load shedding and air emissions. The process of building the models and running for tests indicate that a fair comparison is difficult to obtain due to the multi-dimensional performance metrics considered here, and the difficulty in setting up the parameters of the models in a way that does not advantage or disadvantage one modeling framework. Along these lines, this study explores the effect that model assumptions such as reserve requirements, value of lost load (VOLL) and wind spillage costs have on the comparison of the performance of stochastic vs deterministic market clearing models.