Cost-effective selection of sampling frequencies for regulatory water quality monitoring

A dynamic programming code was formulated for the purpose of assigning sampling frequencies throughout a regulatory water quality monitoring network in order to optimize the statistical performance of the network while operating within a fixed budgetary constraint. The statistical objective is to achieve the greatest possible station to station uniformity in confidence interval widths about annual geometric means of the measured water quality variables and to keep the average confidence width reasonably small. The objective function is the sum (over several selected variables and all stations) of the normalized positive deviations of the predicted confidence interval widths from preselected design confidence interval widths. The code was designed to account for the effects of deterministic seasonal variation and serial correlation of the water quality observations by incorporating the results of the time series analysis of historical quality data. The economic constraint ensures that the annual operating cost of the system, including direct costs of travel and laboratory analysis, will not exceed the allowable budget. As an example situation, the dynamic programming code was used to assign sampling frequencies to the nine stations in Illinois from which historical quality data had been obtained and analyzed. Using five design quality constituents and representative travel and laboratory costs, an “optimal” design was produced. The optimal design achieved a 10% improvement in uniformity (standard deviation) of confidence interval widths when compared to a more traditional design based on the same budget and using identical sampling frequencies at every station.     

Traceability in low-level radioactivity measurements

Various suggested definitions of the term “traceability” are discussed from the standpoint of proposed adoption and use of the concept by the International Organization of Legal Metrology.     

Who cleans up the oil? A microeconomic study

Although oil spill cleanup requirements have existed in the United States for years, recent increases in oil imports and marine transportation of petroleum products as well as growing environmental concern have exposed a new industry, the Oil Spill Cleanup Industry. This paper explores some of the microeconomic aspects of this industry which has come under increased scrutiny by the general public, big business, and the federal government.In addition to a brief history and definition, several basic questions about the economic viability of the oil spill cleanup industry are raised and explored, and the impact on the industry of cleanup from government sources is examined, both from the perspective of present operations and from apparent future increases in federal participation. The primary dilemma facing the industry, that of providing continued and immediate supply while confronted with stochastic demand, is discussed. The effects of the large spill on the industry both in terms of revenue and ability to meet cleanup requirements is also considered.Information for the paper is drawn from past and continuing involvement in the U.S. Coast Guard's Marine Environmental Protection Program by both authors. The statistical evidence presented here was compiled through personal interviews and from two computerized Coast Guard information systems; PIRS (the Pollution Incident Reporting System), and SKIM (the Spill Cleanup Equipment Inventory System).     

A note on natural production functions

Two techniques are presented for estimation of natural animal populations, both of which may incorporate the effect of pollutants on populations. Both techniques assume specific underlying population dynamics which may not be applicable to certain species or ecosystems. However, both techniques allow for testing the hypothesis that the population dynamics specified is applicable. The techniques are used to criticize two recent empirical investigations of fisheries.