Edward A. McBean

Statistical procedures for analysis of environmental monitoring data and risk assessment

1. Characteristics of Environmental Quality Data. I. STATISTICAL MEASURES AND DISTRIBUTIONS. 2. Statistical Characterizations of Data. 3. The Normal or Gaussian Distribution. 4. The Lognormal Distribution. 5. Additional Useful Distributions for Characterizing Environmental Quality Data. II. IDENTIFYING SYSTEM CHANGES. 6. Identification of System Changes and Outliers Using Control Charts and Simple Procedures. 7. Characterizing Coincident Behavior - Regression and Correlation. III. HYPOTHESIS TESTING OF ENVIRONMENTAL QUALITY. 8. Testing Difference Between Monitoring Records: Differences Between Two Locations for Single Constituents. 9. Multiple Comparisons Using Parametric Analyses. 10. Testing Differences Between Monitoring Records When Censored Data Records Exist. 11. Nonparametric Procedures. IV. RISK. 12. Risk Assessment and Data Management. Appendix. Index.

highway stormwater runoff quality: development of surrogate parameter relationships

The development of relationships for predicting the impact of highway stormwater runoff is described. The predictive relationships are regression-based equations reflecting variations in the magnitude of the constituents of interest. The objective is to identify a subset of constituents that can be used as surrogates for the remaining constituents as a means of decreasing the costs of collection and measurement of highway stormwater runoff quality data.The Minnesota highway stormwater quality database complied in the late 1970s and early 1980s is employed in identifying the set of surrogate parameters. The findings indicate that total suspended solids, total dissolved solids, total volatile solids, and total organic carbon are effective surrogate parameters for numerous metals, ionic species, and nutrients. The findings also indicated that the developed ionic species constituent relationships are portable, while the metal and nutrient constituent relationships were limited to urban sites with similar environmental conditions. The development of these relationships represents a step towards remediating and preventing the problem of nonpoint source highway pollution.

Chance-constrained/stochastic linear programming model for acid rain abatement—I. Complete colinearity and noncolinearity

A Linear Programming model is presented for development of acid rain abatement strategies in eastern North America. For a system comprised of 235 large controllable point sources and 83 uncontrolled area sources, it determines the least-cost method of reducing SO2 emissions to satisfy maximum wet sulfur deposition limits at 20 sensitive receptor locations. In this paper, the purely deterministic model is extended to a probabilistic form by incorporating the effects of meteorologic variability on the long-range pollutant transport processes. These processes are represented by source-receptor-specific transfer coefficients. Experiments for quantifying the spatial variability of transfer coefficients showed their distributions to be approximately lognormal with logarithmic standard deviations consistently about unity. Three methods of incorporating second-moment random variable uncertainty into the deterministic LP framework are described: Two-Stage Programming Under Uncertainty (LPUU), Chance-Constrained Programming (CCP) and Stochastic Linear Programming (SLP). A composite CCP-SLP model is developed which embodies the two-dimensional characteristics of transfer coefficient uncertainty. Two probabilistic formulations are described involving complete colinearity and complete noncolinearity for the transfer coefficient covariance-correlation structure. Complete colinearity assumes complete dependence between transfer coefficients. Complete noncolinearity assumes complete independence. The completely colinear and noncolinear formulations are considered extreme bounds in a meteorologic sense and yield abatement strategies of largely didactic value. Such strategies can be characterized as having excessive costs and undesirable deposition results in the completely colinear case and absence of a clearly defined system risk level (other than expected-value) in the noncolinear formulation.

Siloxanes in biogases from landfills and wastewater digesters

Volatile methylsiloxanes and linear polydimethylsiloxanes (siloxanes), generated as components of biogases from digesters at wastewater treatment plants and solid waste landfills, have been identified as potentially causing significant operational problems. The problems are demonstrated as being an issue of “sand in the transmission” and are the reason for the changing of engine warrantees. Examples of monitored concentrations of siloxanes at a series of facilities are described, demonstrating degrees of variability both spatially and temporally, for different biogas sources. Wastewater digesters are shown to be generally producing biogases with higher siloxane concentrations than landfills. With a trend toward some landfills being operated as bioreactors, where the temperatures in the refuse are elevated to levels comparable to those within wastewater treatment digesters, there is potential that landfill-derived biogases may result in increased concentrations of siloxanes and hence more operating problems.

Chance-constrained/stochastic linear programming model for acid rain abatement—II. Limited colinearity

Abstract In Part I of this work, a deterministic model for development of acid rain abatement strategies was extended to a stochastic form through the incorporation of uncertainty in the transfer coefficients which describe long-range pollutant transport and transformation. The two extreme cases of: 1. (i) complete dependence between transfer coefficients (i.e. colinearity); 2. (ii) complete independence (noncolinearity) were developed. In this work, a more realistic ‘middle ground’ between these two extremes is investigated. This approach and its associated transfer coefficient covariance structure involve limited colinearity. A simplified linear version of the limited colinearity optimization model is employed. An application is presented which shows that a central three-state, one-receptor sub-system (‘sub-airshed’) in eastern North America plays a dominant role with respect to determining overall system performance characteristics. Nonlinear, nonlinearnonseparable and multiobjective extensions of the stochastic model are discussed.

Multi-Objective Optimization for Monitoring Sensor Placement in Water Distribution Systems

As water distribution systems are vulnerable to a variety of accidental or deliberate contaminant intrusion events, efficient in-situ water quality monitoring is important in providing a robust water supply. To identify optimal placements of monitoring sensors in water distribution systems, a multiple-objective optimization method employing genetic algorithms (GA) in conjunction with data mining, is developed. The proposed methodology is capable of identifying an optimal set of monitoring stations based on three objectives: detection delay time, detection probability, and the affected population prior to detection. To apply the method, a database which stores data for intrusion events at each node, and the classified consequences of these intrusions at each node, is prepared. The initial solutions for multi-objective optimization are obtained from the database based on sensor coverage criteria. Pareto ranking is performed during the GA optimization. The effectiveness of the proposed method is illustrated by applying the methodology to the two networks, Networks 1 and 2, provided by the Battle of the Water Sensor Networks design competition. The final results in application to Networks 1 and 2 are also provided.

Wetlands: Environmental Gradients, Boundaries, and Buffers

Introduction Environmental Gradients, Boundaries, and Buffers: An Overview, V. Carter Wetlands and Environmental Gradients, M.M. Holland Vertical Gradients in Peatlands, B.G. Warner Urban Intensification and Environmental Sustainability: The Maintenance of Infiltration Gradients, E.A. McBean, G. Mulamoottil, Z. Novak, and G. Bowen Root Zone Moisture Gradients Adjacent to a Cedar Swamp in Southern Ontario, D.E. Stephenson and D.B. Hodgson Adaptive Mechanisms of Plants Occurring in Wetland Gradients, C.W.P.M. Blom, H.M. van de Steeg, and L.A.C.J. Voesenek Practical Considerations for Wetland Identification and Boundary Delineation, R.W. Tiner Toward the Integration of Wetland Functional Boundaries into Suburban Landscapes, W.G. Pearsell and G. Mulamoottil Management Goals and Functional Boundaries of Riparian Forested Wetlands, J.E. Cox The Use of Avian Fauna in Delineating Wetlands in the Baldwin Wetland Complex, Southern Ontario, P.J. Harpley and R.J. Milne Temporal Delineation of Wetlands on Gull Point, Presque Isle, Pennsylania, P.S. Botts and R. Donn A Comparison of Wetland Boundaries Delineated in the Field to Those Boundaries on Existing State and Federal Wetlands Maps in Central New York State, J.M. McMullen and P.A. Meacham Wetland Buffers and Runoff Hydrology, J.D. Phillips Effect of Buffer Strips on Controlling Soil Erosion and Nutrient Losses in Southern Finland, J. Uusi-Kamppa and T. Ylaranta Hydrogeological Criteria for Buffer Zones Between Wetlands and Aggregate Extraction Sites, J.Z. Fraser, D. Routly, A.B.F. Hinton, and K. Richardson The CREAMS Model for Evaluating the Effectiveness of Buffer Strips in Reducing Sediment Loads to Wetlands, K.J. McKague, Y.Z. Cao, and D.E. Stephenson The Use of Vegetative Buffer Strips to Protect Wetlands in Southern Ontario, A.J. Norman Summary of Final Session Index

A management model for acid rain abatement

Abstract A linear programming-based model is developed for examining management alternatives for acid rain abatement. The model reflects both expected values and elements of the stochastic considerations. A case study application of the model demonstrates the impact of cost sharing for the different management alternatives.

Stochastic modelling of the effects of reservoir operation

Abstract Reservoir operation can often significantly alter the mean level of the monthly average flows downstream from a dam. For instance, flows may be increased downstream from the dam during a previous dry season before reservoir construction and decreased when there is abundant runoff. Intervention analysis is a stochastic technique that can be used to model and statistically describe the effects of reservoir operation on the seasonal average downstream flows. The mathematical theory of intervention analysis is explained, followed by the identification, estimation and diagnostic check stages of model development. An intervention model is developed to model the effect of the operation of the Gardiner Dam on the downstream average monthly flows of the South Saskatchewan River at Saskatoon, Canada. It is shown how the percentage changes in mean flows are calculated from the estimated transfer function parameters. Because the standard errors are known for the estimated parameters, confidence intervals for the flow changes can also be obtained.

Water Quality Modeling of the Kali River, India

Water quality monitoring procedures effective in calibrating the QUAL2E model for the Kali River in India are described. The stability of dry season conditions for the Kali River are utilized to consider the migration pathways, and hence the calibration efforts necessary for water quality models. Alternative procedures for calibrating values for the reaction rates are utilized for reinforcement of the findings. These alternatives include changes in stream turbidity which are shown to be a useful measure of benthic oxygen demand. Ratios of BOD5 to COD are reported between sugar mills, industrial inputs and municipal sources.