Co-existing populations of Pacific ocean perch, Sebastes alutus, in Queen Charlotte Sound, British Columbia

Variation at five microsatellite loci (Sal1, Sal2, Sal3, Sal4 and Sal5) was examined in approximately 1300 Pacific ocean perch (Sebastes alutus) sampled from 14 coastal sites in British Columbia, Canada. Mean observed heterozygosities by locus ranged from 71% to 88%, and by sample ranged from 75% to 84%. Theta values ranged from 0 to 0.04 over the five loci, and averaged 0.015. Among Pacific ocean perch samples, Š ranged from 0.001 to 0.056. Canonical discriminate analysis of multilocus genotypes and neighbour-joining analysis of pairwise genetic distances between samples both indicated the presence of three populations, one off the west coast of Vancouver Island (the Vancouver Island population) and two co-existing populations in Queen Charlotte Sound, Dixon Entrance and along the west coast of the Queen Charlotte Islands (the eastern and western QCI populations). Pacific ocean perch of the eastern and western QCI populations were caught in close proximity to each other, but individual samples showed little evidence of admixture. Fall and spring samples collected within geographic areas were genetically similar, indicating seasonally stable population structure. Restricted gene flow between the Vancouver Island and the two more northerly populations may result from limited adult dispersal and larval retention within the California Current and Alaska Gyre, respectively, but the presence of two populations within Queen Charlotte Sound cannot be explained entirely by larval retention hypotheses. The presence of two Pacific ocean perch populations in central British Columbia has implications for fisheries management.     

Electricity from fossil fuels without CO2 emissions: assessing the costs of carbon dioxide capture and sequestration in U.S. electricity markets.

The decoupling of fossil-fueled electricity production from atmospheric CO2 emissions via CO2 capture and sequestration (CCS) is increasingly regarded as an important means of mitigating climate change at a reasonable cost. Engineering analyses of CO2 mitigation typically compare the cost of electricity for a base generation technology to that for a similar plant with CO2 capture and then compute the carbon emissions mitigated per unit of cost. It can be hard to interpret mitigation cost estimates from this plant-level approach when a consistent base technology cannot be identified. In addition, neither engineering analyses nor general equilibrium models can capture the economics of plant dispatch. A realistic assessment of the costs of carbon sequestration as an emissions abatement strategy in the electric sector therefore requires a systems-level analysis. We discuss various frameworks for computing mitigation costs and introduce a simplified model of electric sector planning. Results from a "bottom-up" engineering-economic analysis for a representative U.S. North American Electric Reliability Council (NERC) region illustrate how the penetration of CCS technologies and the dispatch of generating units vary with the price of carbon emissions and thereby determine the relationship between mitigation cost and emissions reduction.     

Experimental verification of a model describing solid phase microextraction (SPME) of freely dissolved organic pollutants in sediment porewater

To verify a theoretical mass balance and multiple compartment partitioning model developed to predict freely dissolved concentrations (FDCs) of hydrophobic organic chemicals (HOCs) using negligible depletion-solid phase microextraction (nd-SPME), a series of sediment slurry experiments were performed using disposable poly(dimethyl)siloxane (PDMS) coated-SPME fibers and (14)C-radiolabeled HOC analogs. First, pre-calibration of disposable PDMS coated fibers for four model compounds (phenanthrene, PCB 52, PCB 153 and p,p'-DDE) with good precision (PCB 52>PCB 153, and the measured and predicted C(pw) values were not substantially different from empirically determined values except for p,p'-DDE.     

Predictions of U.K. regulated power station contributions to regional air pollution and deposition: a model comparison exercise

Contributions of the emissions from a U.K. regulated fossil-fuel power station to regional air pollution and deposition are estimated using four air quality modeling systems for the year 2003. The modeling systems vary in complexity and emphasis in the way they treat atmospheric and chemical processes, and include the Community Multiscale Air Quality (CMAQ) modeling system in its versions 4.6 and 4.7, a nested modeling system that combines long- and short-range impacts (referred to as TRACK-ADMS [Trajectory Model with Atmospheric Chemical Kinetics-Atmospheric Dispersion Modelling System]), and the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model. An evaluation of the baseline calculations against U.K. monitoring network data is performed. The CMAQ modeling system version 4.6 data set is selected as the reference data set for the model footprint comparison. The annual mean air concentration and total deposition footprints are summarized for each modeling system. The footprints of the power station emissions can account for a significant fraction of the local impacts for some species (e.g., more than 50% for SO2 air concentration and non-sea-salt sulfur deposition close to the source) for 2003. The spatial correlation and the coefficient of variation of the root mean square error (CVRMSE) are calculated between each model footprint and that calculated by the CMAQ modeling system version 4.6. The correlation coefficient quantifies model agreement in terms of spatial patterns, and the CVRMSE measures the magnitude of the difference between model footprints. Possible reasons for the differences between model results are discussed. Finally, implications and recommendations for the regulatory assessment of the impact of major industrial sources using regional air quality modeling systems are discussed in the light of results from this case study.     

Identifying Change-Points in Biological Sequences via Sequential Importance Sampling

The genomes of complex organisms, including the human genome, are highly structured. This structure takes the form of segmental patterns of variation in various properties and may be caused by the division of genomes into regions of distinct function, by the contingent evolutionary processes that gave rise to genomes, or by a combination of both. Whatever the cause, identifying the change-points between segments is potentially important, as a means of discovering the functional components of a genome, understanding the evolutionary processes involved, and fully describing genomic architecture. One property of genomes that is known to display a segmental pattern of variation is GC content. The GC content of a portion of DNA is the proportion of GC pairs that it contains. Sharp changes in GC content can be observed in human and other genomes. Such change-points may be the boundaries of functional elements or may play a structural role. We model genome sequences as a multiple change-point process, that is, a process in which sequential data are separated into segments by an unknown number of change-points, with each segment supposed to have been generated by a different process. We consider a Sequential Importance Sampling approach to change-point modeling using Monte Carlo simulation to find estimates of change-points as well as parameters of the process on each segment. Numerical experiments illustrate the effectiveness of the approach. We obtain estimates for the locations of change-points in artificially generated sequences and compare the accuracy of these estimates to those obtained via Markov chain Monte Carlo and a well-known method, IsoFinder. We also provide examples with real data sets to illustrate the usefulness of this method.     

Evaluation of diffusive samplers and photoionisation detectors for measuring very short peak exposures in the workplace

The extent to which very short peak widths, peak frequency, sampling time and post-sampling/pre-capping time impact upon occupational exposure measurements of toluene has been investigated using diffusive tubes. Additionally, the effect of the width of the peak on the estimation of peak maximum concentration and time-weighted average (TWA) concentration from real-time instruments (photoionisation detectors-PIDs) was also studied, and their responses modelled. No clear differences were perceived between diffusive and pumped tube results. Mean biases of -5 to +6% were recorded but no trend could be distinguished with respect to any of the variables examined; the main source of uncertainty was attributed to analytical uncertainty. The diffusive tubes can therefore be used to measure short term transient toluene concentrations (e.g. 5 s duration) over short (15 min) exposure periods. The two slower responding PIDs (t50 = 4 s) underestimated the maximum concentration of short term peaks having durations less than 10 s. The other three PIDs (t50 < or = 2 s) only significantly underestimated the maximum concentration of short term peaks having durations of 2 s and below. Pulse duration appeared to affect the PID's estimation of peak height more than peak area (TWA concentration).     

Regulation of pesticide discharge into surface and groundwater under uncertainty: A model for environmental risk‐profitability tradeoffs and policy selection

The presence of contaminants in environmental media as well as the desire to maintain a high level of economic activity has led to an important and difficult decision‐making problem for both public policy decision makers and the general public. In one sense, all of the interested parties are likely to be concerned about the potential health risks posed by the presence of contaminants in environmental media and the need to design/implement policies for their mitigation and/or removal. At the same time, however, there also appear to be concerns about the cost of these policies. These costs could be measured in terms of the potential losses in economic activity that are likely to occur when a policy is adopted. The policy can be selected from a range of alternatives, with the choice being driven in part by the stakeholders represented in the policy decision problem. In this case, two general goals might be considered: minimizing environmental risk and minimizing the economic impact of the policy considered. These two objectives are likely to be viewed as conflicting goals, and the nature of the tradeoffs between them must be taken into account in the policy selection process. This paper presents the development of a zero–one weighted goal programming model that can be used to select a preferred policy that minimizes surface and groundwater contamination as well as the economic costs of environmental policy selection. A safety rule model is developed first and then extended to the zero–one weighted goal programming formulation. The stochastic aspects of these structures are emphasized throughout. The paper also addresses a number of issues related to implementation of the model.     

Field evaluation of nitrogen availability from fresh and composted manure

Prediction of manure nitrogen availability to crops is key to ensuring adequate production while minimizing potential adverse environmental impacts. This field study estimated first-year and residual N availability from several manures subjected to various levels of processing, and evaluated the applicability of the pre-sidedress soil N test (PSNT) in fields receiving the different manures using corn (Zea mays L.) as the test crop. Plots received several rates of fresh (FP), dried (DP), or composted (CP) poultry (Gallus gallus domesticus) manure, composted cow (Bos taurus) (CC) manure, ammonium nitrate (AN), or no N. Crop yields and N uptake from plots where CC was applied were undistinguishable from controls in most years, whereas poultry manures significantly increased corn production. Average apparent first-year N availability, as measured by fertilizer equivalence, was 57, 53, 14, and 4% for FP, DP, CP, and CC respectively. Apparent second-year N availability, as measured by relative effectiveness, was 18, 19, 12, and 7% for FP, DP, CP, and CC; however, for CC both first- and second-year estimates of apparent N recovery (ANR) could statistically not be separated from the controls. Apparent nitrogen avail-ability was greater for less processed manures and for CP compared to CC, emphasizing that producers should know the source and level of compost stability when these materials are used as a primary nutrient source. The PSNT successfully (87% correct) identified sites with a critical value of 24 mg kg(-1) that were N sufficient across a variety of N amendments from those that would have benefitted from additional N input.     

Intervention analysis in environmental engineering

Intervention analysis techniques are described for identifying and statistically modelling trends which may be present in water quality time series. At the exploratory data analysis stage, simple graphical and modelling methods can be employed for visually detecting and examining trends in a time series caused by one or more external interventions. For instance, a plot of a robust locally weighted regression smooth through a graph of the observations over time may reveal trends and other interesting statistical properties contained in the time series. In addition, statistical tests, such as different versions of the nonparametric Mann-Kendall test, can be used to detect the presence of trends caused by unknown or known external interventions. To characterize rigorously and estimate trends which may be known in advance or else detected using exploratory data analysis studies, different parametric methods can be utilized at the confirmatory data analysis stage. Specifically, the time series modelling approach to intervention analysis can be employed to estimate the magnitudes of the changes in the mean level of the series due to the interventions. Particular types of regression models can also be used for estimating trends, especially when there are many missing observations. To demonstrate how intervention analysis methods can be effectively used in environmental impact assessment, representative applications to water quality time series are presented.Invited Paper for Presentation at The Workshop on Statistical Methods for the Assessment of Point Source Pollution, The Canada Centre for Inland Waters, Burlington, Ontario, Canada, L7R 4A6, September 12–14, 1988.