Multivariate analysis of the impacts of the turbine fuel JP-4 in a microcosm toxicity test with implications for the evaluation of ecosystem dynamics and risk assessment

Abstract

Turbine fuels are often the only aviation fuel available in most of the world. Turbine fuels consist of numerous constituents with varying water solubilities, volatilities and toxicities. This study investigates the toxicity of the water soluble fraction (WSF) of JP-4 using the Standard Aquatic Microcosm (SAM). Multivariate analysis of the complex data, including the relatively new method of nonmetric clustering, was used and compared to more traditional analyses. Particular emphasis is placed on ecosystem dynamics in multivariate space.The WSF is prepared by vigorously mixing the fuel and the SAM microcosm media in a separatory funnel. The water phase, which contains the water-soluble fraction of JP-4 is then collected. The SAM experiment was conducted using concentrations of 0.0, 1.5 and 15% WSF. The WSF is added on day 7 of the experiments by removing 450 ml from each microcosm including the controls, then adding the appropriate amount of toxicant solution and finally bringing the final volume to 3 L with microcosm media. Analysis of the WSF was performed by purge and trap gas chromatography. The organic constituents of the WSF were not recoverable from the water column within several days of the addition of the toxicant. However, the impact of the WSF on the microcosm was apparent. In the highest initial concentration treatment group an algal bloom ensued, generated by the apparent toxicity of the WSF of JP-4 to the daphnids. As the daphnid populations recovered the algal populations decreased to control values. Multivariate methods clearly demonstrated this initial impact along with an additional oscillation seperating the four treatment groups in the latter segment of the experiment. Apparent recovery may be an artifact of the projections used to describe the multivariate data. The variables that were most important in distinguishing the four groups shifted during the course of the 63 day experiment. Even this simple microcosm exhibited a variety of dynamics, with implications for biomonitoring schemes and ecological risk assessments.

Ordination of Zooplankton Community Data to Detect Pesticide Effects in Pond Enclosures

“…In this instance, the multivariate approach did not necessarily increase the statistical power to detect sample differences over the more conventional univariate ANOVA and regression techniques of the previous analyses. However, others have demonstrated that multivariate techniques can reveal biotic response patterns from experimental ecosystems that were not observed using univariate analysis (Landis et al 1993). In the present study, the applications of ordination techniques have been demonstrated, and this multivariate approach is clearly more applicable and appropriate than univariate analyses for the complex data matrices typical of mesocosm experiments.…”

Persistence of the Effects of Jet-a in a Microcosm With Releases From the Sediment

“…Toxicol. However, because of the temporal nature of multispecies testing, problems exist with using conventional ANOVA [20][21][22][23]. 20, 2001 1943 icity tests is the analysis of a large multivariate data set with relatively few replicates compared to the number of features.…”

“…Landis et al [19,22,23] used metric and nonmetric clustering and association analysis (NCAA) as developed by Matthews, Matthews, and coworkers [24][25][26]. Landis et al [19,22,23] used metric and nonmetric clustering and association analysis (NCAA) as developed by Matthews, Matthews, and coworkers [24][25][26].…”

A contrast of human health risk and ecological risk assessment: Risk assessment for an organism versus a complex nonorganismal structure

“…Handling the visual system of a robot, for instance, in a room full of tools, debris, light and shadows, is a classic problem of analyzing large quantities of "dirty" data into meaningful categories. In previous work, we have shown that environmental datasets are amenable to Al techniques (Matthews et al, 1991a(Matthews et al, , 1991bLandis et al, 1993aLandis et al, , 1993bLandis et al, , 1994. In each of these cases, our Al tools filtered a complex dataset to reveal patterns that went unnoticed by unaided human ecologists.…”

“…Our research group (Landis et al, 1993a;1993b;1994) has also described the persistence of information within even simple model ecological systems, the standardized aquatic microcosm (SAM) and the mixed flask culture (MFC). We use tools derived from artificial intelligence (Al) and machine learning research (Matthews, Matthews, and Landis, 1995) to look for patterns within the diverse dataset typical of ecological experiments.…”