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Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
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Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
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Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
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Page 46
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
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Page 47
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
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Page 48
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
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Page 49
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
×
Page 50
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
×
Page 51
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
×
Page 52
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
×
Page 53
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
×
Page 54
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
×
Page 55
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
×
Page 56
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
×
Page 57
Suggested Citation:"SCIENTIFIC TOOLS FOR ENVIRONMENTAL MONITORING." National Research Council. 1987. Agricultural Development and Environmental Research: American and Czechoslovak Perspectives: Proceedings of a Bilateral Workshop. Washington, DC: The National Academies Press. doi: 10.17226/19179.
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Page 58

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Scientific Tools for Environmental Monitoring DAVID N. MCNELIS University of Nevada, Las Vegas In performing environmental assessments, measurements are made either over highly localized or site-specific areas or over con- tinuous or nearly continuous large geographic regions which might involve a number of discrete sites. The term remote sensing as applied to environmental monitoring means simply the acquisition of environmental information without physical contact between the measured feature or parameter and the measurement sensor. There are obviously trade-offs to consider in selecting a measurement and monitoring system—traditional point monitoring techniques or re- mote sensing—including the quality of information and the cost of obtaining that information. REMOTE SENSING Airborne remote sensing measurements are typically employed to provide information over broad regions where the advantages of rapid, wide area, near-simultaneous coverage can be exploited. In addition, these systems typically can provide spatial coverage in time intervals that are consistent with observing the dynamics of pollutant transport, transformation, and depletion. For many applications, particularly in inaccessible locations and in complex terrain, this technology provides the only opportunity to collect certain data sets. Remote sensing systems can be characterized as either active or passive depending on whether the system incorporates an energy source in performing the environmental interrogation. In active sys- tems, some fraction of the transmitted energy (laser or light source) 44

45 is scattered from or absorbed by the target; in passive systems, the measurement depends on the passive reflectance or surface emission of energy from the area of interest. Both active and passive remote sensing methods have applica- tion in environmental monitoring with regard to agriculture-related investigations. A few of the hardware systems are briefly described in this paper, with most of the emphasis being placed on their potential applications. Passive Remote Sensing Systems Photography, the most widely used remote sensing technique, has been used for over a century and, as a result, enjoys by far the most advanced development in terms of instrumentation, materials, and interpretation. More recently, thermal scanners and multispec- tral scanners have gained widespread use. While both of these tech- niques yield synoptic views that may be useful in the extrapolation from point or limited area measurement data, neither technique holds much promise for the identification of specific contaminants. Nevertheless, photography continues to be the primary remote sensing technique due to several factors: the well-developed tech- niques, materials, and equipment; the tremendous information con- tent of an individual image; and the ease of interpretation of data. Also, photography often serves to complement in situ measurement data. The multispectral scanner (MSS) class of systems is based on the distribution of radiant energy from the ground surface over a number of discrete energy bands. The energy level in each band is digitized and recorded separately, and various combinations of bands may be used in interpretation. The bands may extend from the ultra- violet, through the visible and near-infrared, and into the thermal infrared region of the electromagnetic spectrum. The techniques are well-developed, both from the standpoint of scanner hardware and interpretative software. The devices have been used extensively on satellite platforms, but they are also flown on aircraft to obtain even greater image resolution. Landsat 4 was primarily designed for agricultural applications and crop inventories. The satellite's orbital altitude, along with its seven spectral bands, has made it particularly useful for this purpose as well as for a number of other environmental and geologic applica- tions. The band widths sensed and the pixel or picture element size

46 TABLE 1 Sensor characteristic* (Landsat 4 - TM) Band Wavelength Sensed Pixel Sice (in micrometers) (in meters) 1 0.45 - 0.52 80 1 0.52 - 0.60 30 8 0.6S - 0.69 SO 4 0.76 - 0.90 30 5 1.55 - 1.75 30 6 10.4 - 12.5 120 7 2.08 - 2.35 80 TABLE 2 Sensor characteristics (SPOT) Wavelength Sensed (in micrometers) Pixel Size (in meters) Multispectral Mode 0.50 - 0.59 0.61 - 0.68 0.79 - 0.89 SO Panchromatic Mode 0.51 - 0.73 10 for Landsat 4 with its thematic mapper (TM) are shown in Table 1. The Landsat 4 - TM system has about 227 million pixels when all seven bands are used. The increase in resolution over the previous MSS systems (79-m pixel size) with this newer system (30-m pixel size) permits many more structural and morphologic features to be mapped. The French System Probatoire d'Observation de la Terre (SPOT) carries two high-resolution visible sensors which can operate either in a multispectral mode or in a high-resolution panchromatic mode. In addition, the field of view can be directed 27° to either side of the ground track. This feature makes it possible to view a critical area on consecutive days and also, by virtue of the difference in view angles, to obtain stereoscopic coverage. The operating characteristics of the SPOT sensors are shown in Table 2. Active Remote Sensing Systems Table 3 summarizes the scattering and absorption processes employed in laser systems and indicates the remote measurement

47 TABLE 3 Summary of applicable processes for remote sensing Scattering Rayleigh Elastic l.^l, Lidar MIE Elastic f J = rj Lidar Raman Inelastic ^ X fd Lidar Fluorescence Inelastic f, /£ f. Fluorosensor Absorption Absorption due to vibrations, IR, Visible Rotational and/or electronic and UV Dial f = laser frequency f, = detected frequency methods which are based on these principles. The design concept and selected practical environmental measurement applications for two of these remote sensing instruments—the lidar and the laser fluorosensor—are discussed below. In addition, Figure 1 shows the absorption features of many pollutant gases over the region of < 1 - 15 um (Yates and Taylor 1960). The range-resolved concentra- tions of several of these gases can also be quantitatively monitored through the use of the differential absorption lidar (DIAL); however, this system will not be discussed further in this paper. Lidar Devices Classical lidar devices are used to profile aerosol distributions in the lower atmosphere. Measurements are made by observing the relative backscattering of the intense, extremely short pulse of laser light as it interacts with suspended particles and molecules. Elec- tronic analyzers monitor the elapsed time from firing the laser to the scattering returns and thereby range or measure the distances to the aerosol layers and the relative concentration of aerosol in discrete volumes within those layers. The airborne lidar in operational use by the U.S. Environmental Protection Agency (EPA) is a two-frequency system consisting of a neodynium-YAG laser transmitter, a 36-cm Newtonian telescope receiver, and an electronics package which provides for real-time processing and displaying of the range-resolved backscatter from aerosols between the aircraft and the ground and the surface-reflected energy, both at transmitted wavelengths. A flow chart of the system

48 if li DYE •«—•» i DYE x 2 Tunable diode laser l 13 Wavelength, n.m FIGURE 1 Absorption features of various gases. is shown in Figure 2. The two wavelengths, one in the green (0.53 um) portion of the visible spectrum and the other in the near infrared (1.06 um), are emitted simultaneously with a firing rate that can be varied between one and 10 Hz. At the maximum firing rate and when combined with typical operational air speeds, a horizontal resolution of about 10 m can be obtained. With a laser pulse width of 20 nsec, a maximum vertical resolution of 3 m is likewise obtainable although the signal is usually digitized to yield a resolution of 6 m. Some of the typical applications of lidars are: • Determining the space and time variability of aerosol inhomo- geneities; • Assessing relative particulate concentrations; • Measuring the vertical growth of boundary layers; • Providing input to complex terrain and other atmospheric dis- persion modeling efforts; • Measuring plume opacity;

49 16-bit A/D conveners High 9-track magnetic tape Navigation (Loran C) T 50 MHZ | Profiles \ / Bit slice processor _j Z-80 Microcomputer Function: Background subtract Laser power correction High resolution profile Buffer _ Memory 6.4 Shots Function: Real time display • Tape deck control ~~ Tag word generation S-100 BUS Camac Bus / \ 1 Real Time Clock Control monitor & keyboard Transmitter/Receiver FIGURE 2 Airborne lidar schematic. • Investigating the mixing of multiple boundary layers using a series of injected fluorescent dye particles; • Positioning in situ-sensor aircraft in an air mass or plume. One lidar experiment made use of indigenous aerosols to study pollutant transport in the complex coastal environment of southern California (McElroy and Smith 1986). Although the specific applica- tion under discussion relates to a coastal environment, several other studies have been conducted well inland to monitor and track the plume created during crop residue burn-offs or from industrial stacks. The procedures employed in the interpretation of the lidar returns are not unlike those described in this experiment. Laser Fluorosensor Initially, airborne laser fluorosensing systems were developed to locate and identify the source of petrochemicals in lakes and river systems. Subsequently, developmental efforts were directed at a num- ber of other water quality parameters. Operational airborne systems are hi use by a number of research centers including the Environ- mental Monitoring Systems Laboratory in Las Vegas, Nevada; the Canadian Center for Remote Sensing in Ontario, Canada; the Uni- versity of Oldenburg, FRG; and the National Aeronautics and Space

50 Administration (NASA) facility at Wallops Island, Virginia. Several applications for laser fluorosensor systems are: • Optical attenuation coefficient measurements; • Observations of the mixing and interfacing of marine and fresh waters; • Locating, mapping, and fingerprinting petrochemical spills and discharges; • Assessing the relative contribution of point and non-point source pollution to receiving waters; • Flow, dispersion, and mixing studies using fluorescent dyes; • Bathymetry; • Column or range-resolved concentration measurements of algal pigments; • Estimating acidification parameters in surface waters, e.g., pH, [Al], [DOC], [HCOs], [S04]. Existing systems are capable of mapping changes in concentra- tions of chlorophyll a and dissolved organic carbon (DOC). The chlorophyll a concentration is an indicator of phytoplankton (plank- tonic algae) activity, and high levels result from high nutrient levels introduced from sewage effluents and agricultural runoff. The con- centration of DOC in surface waters indicates the carbon equivalent of the naturally occurring and anthropogenically produced dissolved organic materials present. A typical fluorescence emission spectrum for the measurement of those two water quality parameters which also permits an estimate of the optical attenuation coefficient for the water column is shown in Figure 3. A broad fluorescence band resulting from the excitation at 475 nm and which peaks at about 540 nm is generally accepted as due to dissolved organic matter (DOM) in the water. Also observable in the spectrum is the fluorescence band due to the presence of chlorophyll a and a Raman emission band from the OH vibrational stretching mode of the water molecules. As that latter peak is a property of only water, and the Raman emission cross section is only weakly dependent on salinity and temperature, it has been demonstrated to be effective as an indicator of changes in optical attenuation, particularly in fresh waters measured at a fixed wavelength (Bristow et al. 1981). A demonstration of this system, which included correlations of DOC in addition to chlorophyll a and the optical attenuation coefficient, was conducted along the Columbia and Snake rivers in

51 Fluorescence and Raman Laser-Excitation at 475nm -Not to Scale 0-H Stretch Water Raman Band at 567nm Fluorescence Band due to Dissolved Organics 1n Vivo Chlorophyll a Fluorescence Band at 685 nm 450 500 550 600 650 FIGURE 3 Typical fluorescence emission spectrum. TOO 750 the state of Washington. In addition to the ability of the laser fluorosensor to measure several parameters simultaneously, it was concluded as a result of this study that the interrelationships between the various parameters measured was of greater significance than the measures of the parameters themselves in removing ambiguities and providing insight into otherwise anomalous data (Bristow et al. 1985). The airborne laser fluorosensor data generally exhibited a simple linear relationship with the measured water quality parameters. The system was also able to measure chlorophyll a and DOC in highly turbid and generally inaccessible fresh water reaches of the rivers. GEOGRAPHIC INFORMATION SYSTEMS (GIS) A primary characteristic of monitoring networks and particu- larly remote sensing systems is that they produce a vast amount of spatially related data. Effective utilization of these large data sets depends on efficient geographic information systems (GIS) to pro- cess and convert the information into a usable form. These systems, which are relatively new, are designed to accept vast amounts of

52 Original Data •». Transformation +*- Generated Data •*- Analysts fl 24.000 USGS Ouadrir FIGURE 4 CIS concept. spatial data from a variety of sources, including remotely sensed, and to efficiently store, retrieve, manipulate, analyze, and display these according to user-defined specifications. A schematic of the GIS concept is shown in Figure 4. All environmental and geologic features can be represented by one of three forms: points, lines, or areas. A feature that at a great distance may be represented by a point may require a polygon to represent its spatial features from a closer perspective. These forms can also be exactly positioned by coordinate systems which provide measurements of such attributes as perimeter length, width, area, and distance and direction between the features. The GIS must be structured to deal with many non-spatial at- tributes of these features. The population within a census tract or city or the crop yield from a particular farm are examples of such non-spatial attributes. Both spatial and non-spatial attributes must be digitally represented in the GIS. A demonstration of the use of a GIS was recently completed for a hazardous waste area in the San Gabriel Basin in southern Cali- fornia (Fenstermaker and Duggan 1986). This 739 square kilometer basin includes 447 wells which provide water for culinary use and for agricultural and industrial purposes. In addition to a number of specific sites, the basin in its entirety is being treated as a hazardous waste site. Elevated levels of trichloroethylene, perchloroethylene, and carbon tetrachloride have been measured in a number of wells within the basin.

53 Certain feature attributes and relationships between these at- tributes were examined in order to characterize the groundwater contamination and its impact on the San Gabriel Basin. Specific objectives of this study were to exercise the GIS by describing the cultural and physical surface and underground aquifer features; mod- eling the groundwater flow patterns and contamination isoconcentra- tion contours over time; displaying the sources of the groundwater contamination through reverse-trajectory analyses; and demonstrat- ing procedures to update the data base. Included within the GIS data base were other data depicting land use (13 classifications), municipal and census tract boundaries, contamination levels in individual wells, and water purveyor districts. These spatially related data sets were used to depict graphically the population potentially impacted in each of the water purveyor districts which included contaminated wells. A two-dimensional, Lagrangian flow pathline groundwater model was used to estimate the mean trajectories and transport times for the pollutants to travel from their sources to the well sites. Average potentiometric surface data were used to minimize abrupt changes in direction. Five such data sets were used spanning the period from 1950-1980. The results of this effort for a polygon in the center of the basin which contained 21 contaminated wells provided a series of 10 pathlines from each well to their probable sources of contamina- tion. It was hypothesized that the contamination actually occurred somewhere along or around the endpoints of those pathlines. The application of this technology to other interest areas such as land and resource management, urban development, marketing, and traffic planning can be readily envisioned. A partial listing of the requirements for such endeavors could include some of the following data types: Flora and Fauna Critical Habitats Threatened/Endangered Species Soil Porosity Soil Morphology Mineral Composition Moisture Content Texture Aquifer Extent Depth to Aquifer Zone of Recharge Flow Rate/Direction Hydraulic Conductivity Permeability Groundwater Quality Meteorology/Climatology • Dissolved Organics • Precipitation Record • pH • Temperature • Salinity • Wind Speed/Direction

54 • Total Dissolved Solids • Temperature Gradient Biological Activity (Soils) Soil Chemistry Hydrology • Adsorption Coefficients Topography • Exchange Capacity Slope/Aspect/Elevation • Ion Speciation Flood Prone Zones • Mineral Content Depth to Bedrock • Leachability Geographic Features • Solubility QUALITY ASSURANCE Basic analytical methods and instrumentation are fairly well standardized internationally. The discussion which follows, there- fore, focuses on analytical quality assurance (QA) and some of the statistical methods utilized to determine the acceptability of data and to assess the relative performance of analytical laboratories. To demonstrate these procedures, a program currently being carried out by the World Health Organization (WHO) and a number of participating countries is presented. Sweden, Yugoslavia, Brazil, Japan, the People's Republic of China, and the United States plan to conduct human exposure as- sessment programs at sites in their respective countries. Initially, both exposure levels (e.g., mother's milk, blood, duplicate diets, and inhalation) and environmental levels (e.g., air, water, and soil) of a number of pesticides will be monitored. A pre-qualification phase of the program involves an evaluation of the capabilities of the various laboratories to analyze the study analytes in a number of matrices. A number of performance evaluation materials (PEMs) were therefore developed and distributed to each country. A detailed methodology was also distributed along with such items as gas chromatography column packing materials, analytical standards, and sample prepa- ration chemicals. These materials were provided to insure that the same methods and lots of chemicals were used by the participants, thereby eliminating any possibility of variation due to traceability or uniformity issues. A summary of the results received to date for this qualification phase of the program follows. Table 4 shows the analytes and their actual concentrations in the various sample matrices. The soybean oil served as a surrogate for human milk, the butterfat served as a fatty food surrogate, and the porcine adipose served as a human adipose tissue surrogate. The standard analytical procedures for pesticides required that each

55 TABLE 4 Performance Evaluation Materials (PEMs) concentrations (ppb) Sample Matrix DDT ODD DDE HCB -HCH Soybean Oil 90 20 18.8 Blood (Low Level) (High Level) 12.5 125 9.44 94.4 11.24 112.4 16.94 169.4 8.64 86.4 Adipose (Low Level) (High Level) 59.0 599 51.9 519 169.5 1695 39.9 399 42.7 427 Butterfat (Low Level) (High Level) 250 990 990 2000 120 240 500 Water (Low Level) (High Level) 1.5 14.97 1.2 0.82 8.1 0.90 8.97 1.02 10.2 12.04 Soil (Low Level) (High Level) 119.8 1197.6 100.3 1003.2 105.5 1055.0 69.8 698 84.7 846.7 sample be divided into three aliquots. A matrix spike (p,p'-DDT) was added to two of the aliquots to provide a measure of any matrix effects. Finally, a surrogate spike (hexabromobenzene) was added to each of the three aliquots as well as to a reagent blank. This spike provided a quantitative measure of the extraction efficiency in each of the samples (i.e., three aliquots plus the reagent blank). The matrix spike would also serve as the surrogate spike if none of the spiking analytes was already present in the sample. In its Pesticide Intercomparison Study, EPA provides an indi- cation of the relative performance of various laboratories (Sovocool and Kantor 1986). The method combines the laboratory's fractional recovery with its ability to qualitatively identify all of the unknown analytes. The fractional recovery is denned as the ratio of the re- ported value to the reference value if that ratio is not greater than one; otherwise, it is the ratio of the difference between twice the reference value and the reported value to the reference value. An example of this procedure applied to this pesticide PEM program is shown in Table 5 for the butterfat matrix. A 10 percent qualitative scoring was arbitrarily selected for the proper identification of all of the analytes, and 90 percent of the overall score was reserved for the quantitative results. Prediction intervals are used by EPA to establish analytical ac- ceptance windows in its Waste Water and Water Quality Programs

66 TABLE 5 Relative performance (low butterfat) DDT DDE HCB 90% 10% 100% Quant Qual Overall Score Score Score True Values 250 990 120 LabU 115 899 71.8 72 8 80 H 259 1060 50.7 69 10 n 0 223 854 148 76 10 86 B 240 1012 144 78 10 M such as Drinking Water Laboratory Certification Program, Point and Non-Point Source Discharge Monitoring, and NPDES Permit Dischargers Program (Britton and Lewis 1986). Figure 5 demon- strates an adaptation of that method to the PEM results. After removing obvious outliers, one measurement was randomly selected from each laboratory's report on these n-selected measurements, and then the interval end points were calculated by the formula, where x and s are the usual sample mean and standard deviation, and t is the upper 97.5 percentile of the Student's ^-distribution with (n- 1) degrees of freedom. A properly designed quality assurance program includes the iden- tification and quantification of all sources of error associated with each step of the environmental monitoring task so that the result- ing data will be of known quality. The components of error, or variance, include those associated with sampling, sample prepara- tion, and analysis. In the past, the major emphasis was placed on analytical QA, although it is now recognized that the component of variance associated with sampling in an inhomogeneous medium such as soils may far exceed that associated with the analytical procedures. Guidelines for soils, vegetation, and sediment sampling quality assurance have recently been developed by EPA, and similar guidelines for groundwater are underway.

57 Butterfat Low 1700 -i- 1459 CD i_ 4-> C 9 o C O O 1300 - R - 900 - 613 High 3400 T 2847 - 2600 R - 1800 - 1368 H U 500 -•- 1000 -1- FIGURE 5 Acceptance window for DDE using prediction intervals. REFERENCES Bristow, M., D. Nielsen, D. Bundy, and R. Purtek. 1981. Use of water Raman emission to correct airborne laser fluorosensor data for effects of water optical attenuation. Applied Optics. Vol. 20. No. 17:2889-2906. Bristow, M.P.F., D.H. Bundy, C.M. Edmonds, P.E. Ponto, B.E. Frey, and L.F. Small. 1985. Airborne laser fluorosensor survey of the Columbia and Snake rivers: Simultaneous measurements of chlorophyll, dissolved organics and optical attenuation. Int. J. Remote Sensing. Vol. 6. No. 11:1707-1734. Britton, P.W., and D.F. Lewis. 1986. Statistical basis for laboratory perfor- mance evaluation limits. U.S. Environmental Protection Agency, Quality Assurance Branch. Draft Report. Cincinnati, Ohio. Fenstermaker, L.K., and J.S. Duggan. 1986. San Gabriel Basin Geographic Information System demonstration. U.S. Environmental Protection Agency. TS-AMD-85742. Las Vegas, Nevada. McElroy, J.L., and T.B. Smith. 1986. Vertical pollutant distributions and bound- ary layer structure observed by airborne lidar near the complex southern California coastline. Atmospheric Environment. Vol. 20. No. 8:1555-1566. Sovocool, G.W., and E.J. Kantor. 1986. FY-85 annual report of the inter- comparison program. U.S. Environmental Protection Agency. Las Vegas, Nevada.

58 Uthe, E.B., W. Vieiee, B.M. Morley, and J.K.S. Ching. 1985. Airborne lidar tracking of fluorescent tracers for atmospheric transport and diffusion studies. Bulletin of the American Meteorological Society. Vol. 66. No. 10:1255-1262. Vahter, M., ed. 1982. Assessment of human exposure to lead and cadmium through biological monitoring. National Swedish Institute of Environmental Medicine and Department of Environmental Hygiene, Karolinska Institute. Stockholm, Sweden. Yates, H.W., and J.H. Taylor. 1960. Infrared transmission of the atmosphere. U.S. Naval Research Laboratory. Report 5453 (ASTIA AD 240188). Wash- ington, D.C. Youden, W.J., and E.H. Steiner. 1975. Statistical manual of the Association of Official Analytical Chemists. Association of Official Analytical Chemists.

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