QUALITY ASSURANCE TECHNIQUES FOR ELECTRONIC DATA ACQUISITION1

ABSTRACT: Electronic instruments are increasingly being used to gather water quality data. Quality assurance protocols are needed which provide adequate documentation of the procedures followed in calibration, collection, and validation of electronically acquired data. The level of precision of many data loggers exceeds the technology which is commonly used to make field measurements. Overcoming this problem involves using laboratory quality equipment in the field or enhanced quality control at the time of instrument servicing. Time control procedures for data loggers are needed to allow direct comparisons of data between instruments. Electronic instruments provide a mechanism to study transient events in great detail, but, without time controls, multiple loggers produce data which contain artifacts due to timing errors. Individual sensors deployed with data loggers are subject to different degrees of drift over time. Certain measurements can be measured with defined precision and accuracy for long periods of time, while other sensors are subject to loss of both precision and accuracy with increasing time of use. Adequate quality assurance requires the levels of precision and accuracy be documented, particularly those which vary with increasing time deployment.     

The mine permitting process

A comparison is presented between environmental regulations and the regulatory institutions of Chile and the USA. There are two important points to the comparison. First, is one system more efficient than the other? Second, does each system result in a comparable level of environmental protection? While an answer to the latter question requires a baseline of data that is currently unavailable, the question of efficiency can be addressed. The Chilean system is found to be more centralized than that of the USA, with more definitive timelines for the permitting process. The US system is more complex and offers more opportunities for public participation in the permitting process. An unequivocal statement as to which is best or most efficient depends on one's perspective regarding the trade off between economic development and environmental quality.     

Postural sway following prolonged exposure to an inclined surface

Fatal and non-fatal falls from elevation have been documented to be a significant issue. Historically, roofing work has been ranked among the highest in incidents of falls. Recent literature has indicated that exposure to inclined surfaces (roof) has a detrimental effect on postural stability. The purpose of this preliminary study was to determine if postural instability was found to increase following extended exposure to an inclined surface. Twenty-three participants were pre/post tested under four postural stability conditions while varying duration exposure (0.0–2.0 h) to an inclined surface. Dependent variable postural sway measures were calculated from center of pressure data derived from a portable force plate. The results indicate significant differences between pre and post-exposure measures. Specifically, the results imply that an individual is less stable directly after performing tasks on an inclined surface. These findings contribute to the literature and serve as a basis for further research into work rest cycles for those individuals who routinely work on inclined surfaces.     

Nitrogen removal and nitrate leaching for forage systems receiving dairy effluent

Florida dairies need year-round forage systems that prevent loss of N to ground water from waste effluent sprayfields. Our purpose was to quantify forage N removal and monitor nitrate N (NO3(-)-N) concentrations in soil water below the rooting zone for two forage systems during four 12-mo cycles (1996-2000). Soil in the sprayfield is an excessively drained Kershaw sand (thermic, uncoated Typic Quartzipsamment). Over four cycles, average loading rates of effluent N were 500, 690, and 910 kg ha(-1) per cycle. Nitrogen removed by the bermudagrass (Cynodon spp.)-rye (Secale cereale L.) system (BR) during the first three cycles was 465 kg ha(-1) per cycle for the low loading rate, 528 kg ha(-1) for the medium rate, and 585 kg ha(-1) for the high. For the corn (Zea mays L.)-forage sorghum [Sorghum bicolor (L.) Moench]-rye system (CSR), N removals were 320 kg ha(-1) per cycle for the low rate, 327 kg ha(-1) for the medium, and 378 kg ha(-1) for the high. The higher N removals for BR were attributed to higher N concentration in bermudagrass (18.1-24.2 g kg(-1)) than in corn and forage sorghum (10.3-14.7 g kg(-1)). Dry matter yield declined in the fourth cycle for bermudagrass but N removal continued to be higher for BR than CSR. The BR system was much more effective at preventing NO3(-)-N leaching. For CSR, NO3(-)-N levels in soil water (1.5 m below surface) increased steeply during the period between the harvest of one forage and canopy dosure of the next. Overall, the BR system was better than CSR at removing N from the soil and maintaining low NO3(-)-N concentrations below the rooting zone.     

UTILIZATION OF LANDSCAPE INDICATORS TO MODEL POTENTIAL PATHOGEN IMPAIRED WATERS1

ABSTRACT: Many water bodies within the United States are contaminated by non‐point source (NPS) pollution, which is defined as those materials posing a threat to water quality arising from a number of individual sources and diffused through hydrologic processes. One such NPS pollutant that is of critical concern are pathogens derived from animal wastes, including humans. The potential presence of pathogens is identified by testing the water for fecal conform, a bacteria also associated with animal wastes. Water contaminated by animal wastes are most often associated with urban and agricultural areas, thus it is postulated that by utilizing land cover indicators, those water bodies that may be at risk of fecal coliform contamination may be identified. This study utilizes land cover information derived from the Multi‐Resolution Land Characterization (MRLC) project to analyze fecal coliform contamination in South Carolina. Also utilized are 14 digit hydro‐logic unit code (HUC) watersheds of the state, a digital elevation model, and test point data stating whether fecal coliform levels exceeded State Water Quality Standards. Proportions of the various land covers are identified within the individual watersheds and then analyzed using a logistic regression. The results reveal that watersheds with large proportions of urban land cover and agriculture on steep slopes had a very high probability of being impaired. (KEY TERMS: Geographic Information Systems; land use planning; nonpoint source pollution; statistical analysis; water quality; watershed management.)     

Estimating abundance of killer whales in the nearshore waters of the Gulf of Alaska and Aleutian Islands using line-transect sampling

Killer whale (Orcinus orca Linnaeus, 1758) abundance in the North Pacific is known only for a few populations for which extensive longitudinal data are available, with little quantitative data from more remote regions. Line-transect ship surveys were conducted in July and August of 2001–2003 in coastal waters of the western Gulf of Alaska and the Aleutian Islands. Conventional and Multiple Covariate Distance Sampling methods were used to estimate the abundance of different killer whale ecotypes, which were distinguished based upon morphological and genetic data. Abundance was calculated separately for two data sets that differed in the method by which killer whale group size data were obtained. Initial group size (IGS) data corresponded to estimates of group size at the time of first sighting, and post-encounter group size (PEGS) corresponded to estimates made after closely approaching sighted groups. ‘Resident’-type (fish-eating) killer whales were more abundant than the ‘transient’-type (mammal-eating). Abundance estimates of resident killer whales (991 [95% CI = 379–2,585] [IGS] and 1,587 [95% CI = 608–4,140] [PEGS]), were at least four times greater than those of the transient killer whales (200 [95% CI = 81–488] [IGS] and 251 [95% CI = 97–644] whales [PEGS]). The IGS estimate of abundance is preferred for resident killer whales because the estimate based on PEGS data may show an upward bias. The PEGS estimate of abundance is likely more accurate for transients. Residents were most abundant near Kodiak Island in the northern Gulf of Alaska, around Umnak and Unalaska Islands in the eastern Aleutians, and in Seguam Pass in the central Aleutians. This ecotype was not observed between 156 and 164°W, south of the Alaska Peninsula. In contrast, transient killer whale sightings were found at higher densities south of the Alaska Peninsula between the Shumagin Islands and the eastern Aleutians. Only two sightings of ‘offshore’-type killer whales were recorded during the surveys, one northeast of Unalaska Island and the other south of Kodiak Island. These are the first estimates of abundance of killer whale ecotypes in the Aleutian Islands and Alaska Peninsula area and provide a baseline for quantifying the role of these top predators in their ecosystem. Electronic Supplementary Material  Supplementary material is available in the online version of this article at and is accessible for authorized users.

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Photographic mark-recapture analysis of clustered mammal-eating killer whales around the Aleutian Islands and Gulf of Alaska

We used photographic mark-recapture methods to estimate the number of mammal-eating “transient” killer whales using the coastal waters from the central Gulf of Alaska to the central Aleutian Islands, around breeding rookeries of endangered Steller sea lions. We identified 154 individual killer whales from 6,489 photographs collected between July 2001 and August 2003. A Bayesian mixture model estimated seven distinct clusters (95% probability interval = 7–10) of individuals that were differentially covered by 14 boat-based surveys exhibiting varying degrees of association in space and time. Markov Chain Monte Carlo methods were used to sample identification probabilities across the distribution of clusters to estimate a total of 345 identified and undetected whales (95% probability interval = 255–487). Estimates of covariance between surveys, in terms of their coverage of these clusters, indicated spatial population structure and seasonal movements from these near-shore waters, suggesting spatial and temporal variation in the predation pressure on coastal marine mammals.