A model for positive environmental control

In the control of pollution, two apparently important facts are often negelected: (1) removal of a particular pollutant from a particular source and location may merely result in transforming it into a pollutant, or pollutants of different forms, in other locations; (2) the activity of pollution control itself requires inputs, the production of which may generate additional pollutants. In order to achieve positive environmental control, these two facts must be considered in selecting control processes and in determining optimum control standards. It is necessary that an integrated approach to pollution control be taken, making allowances for ecological inter-relations. As practiced today, pollution control is handled as separate problems of disposing gas, liquid and solid wastes. This paper proposes a model for evaluating the total environmental impact of a pollution control process or method. Theoretical criteria for determining an optimum control level with or without resource constraint are also derived.     

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.     

Intertemporal and intergenerational pareto efficiency: A comment

In a recent issue of this Journal, Sandler and Smith developed an analysis from which they concluded that discounting is Pareto inefficient in an intertemporal context. This comment questions the validity of that conclusion and demonstrates the essential role played by the discount rate in achieving intertemporal Pareto efficiency within a market economy.     

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.