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.     

Deposition velocities for full-scale corn and soybean canopies: A wind tunnel simulation

Deposition velocities have been determined for corn and soybeans in the first 4–6 weeks of growth in a full-scale study of canopy flow in a wind tunnel. Particles of 1, 5, 10 and 15 μm aerodynamic diameter made of sodium florescein were injected into the Environmental Wind Tunnel Facility at Colorado State University. Deposition velocities were determined as a function of free stream velocity (183, 305 and 610 cm/s) and approach flow turbulence intensity (～1% and 8%). Plants were arranged in realistic field configurations. Hot-wire anemometer studies confirmed that the fluid velocity profiles developed in the wind tunnel were similar to the flow realized in canopies in natural fields. An increase in velocity and turbulence intensity was found to decrease the deposition velocities. A minimum deposition velocity was observed at a particle diameter of 5 μm.     

Threshold and dose response estimation using the “filter model”

The “filter model” has been developed to explain the biologic effects of radiation and chemicals. We have examined nearly 300 sets of dose response data, of which 50 are presented here. Responses (induced by radiation and chemicals) which have been examined include in vitro survival studies on animal and plant tissues, induction of cellular aberrations and time to tumor or death. Similar data from in vivo studies has also been examined. All of the data appear to fit the model R = a lnD + b(lnD)² + c, where R is the response, a and b are parameters fitted by regression to a particular set of data, and c is the response at zero (or lowest) dose. By writing this model in exponential form, it can be seen that the response R results from multistage filtering (by net amounts a and b) of the initial dose, D. The threshold is obtained from this model as the point, $\text{D}\text{?}{}_{\mathrm{T}}$, at which the second derivative becomes zero. This is given by $\text{D}\text{?}{}_{\mathrm{T}}\text{=}\text{exp}\text{(1 ?}\text{a}\text{2b}\text{)}$ when a and b are oppositelt signed.     

A note on the allocation of investment

It is shown by example that the results obtained by Gruver [11 concerning specialization of investment in either directly productive or pollution control capital arise because of an implicit linearity assumption on the underlying technology. Utilizing a strictly concave “eighth sphere” technology, results are obtained which imply joint investment in the two types of capital except in “rare” instances. The results indicate the need for incorporating more general technologies into the analysis of such problems.     

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.