The ecological effects of trichloroacetic acid in the environment

Trichloroacetic acid (TCAA) is a member of the family of compounds known as chloroacetic acids, which includes mono-, di- and trichloroacetic acid. The significant property these compounds share is that they are all phytotoxic. TCAA once was widely used as a potent herbicide. However, long after TCAA's use as a herbicide was discontinued, its presence is still detected in the environment in various compartments. Methods for quantifying TCAA in aqueous and solid samples are summarized. Concentrations in various environmental compartments are presented, with a discussion of the possible formation of TCAA through natural processes. Concentrations of TCAA found to be toxic to aquatic and terrestrial organisms in laboratory and field studies were compiled and used to estimate risk quotients for soil and surface waters. TCAA levels in most water bodies not directly affected by point sources appear to be well below toxicity levels for the most sensitive aquatic organisms. Given the phytotoxicity of TCAA, aquatic plants and phytoplankton would be the aquatic species to monitor for potential effects. Given the concentrations of TCAA measured in various soils, there appears to be a risk to terrestrial organisms. Soil uptake of TCAA by plants has been shown to be rapid. Also, combined uptake of TCAA from soil and directly from the atmosphere has been shown. Therefore, risk quotients derived from soil exposure may underestimate the risk TCAA poses to plants. Moreover, TCE and TCA have been shown to be taken up by plants and converted to TCAA, thus leading to an additional exposure route. Mono- and di-chloroacetic acids can co-occur with TCAA in the atmosphere and soil and are more phytotoxic than TCAA. The cumulative effects of TCAA and compounds with similar toxic effects found in air and soil must be considered in subsequent terrestrial ecosystem risk assessments.     

Screening-level Assessment of Airborne Carcinogen Risks from Uncontrolled Waste Sites

This report presents screening-level estimates of the general level of cancer risks arising from air emission from uncontrolled waste sites. Twenty-five National Priorities List sites were chosen randomly and airborne cancer risks estimated for each site in terms of risk to the maximally exposed individual (MEI risk), average individual risk (AEI risk), and population incidence. The estimates were developed using the EPA Human Exposure Model using assumptions on the rate and toxicity of site emissions.

MEIrisks ranged from 4 × 10^-9 to 1 × 10^-6 with an average of about 5 × 10^-7. AEI risks for individuals residing within four miles of the sites average about 10^-8, declining significantly for individuals residing at longer distances. Population incidence was low at all sites ranging from 2 × 10^-4 to 1 × 10^-2 cancer cases expected within 60 miles of the sites. Due to the uncertainties in this type of analysis and the underlying study assumptions, these results must be viewed with caution. Nonetheless, some preliminary conclusions can be drawn from the analysis, principally that airborne cancer risks from uncontrolled waste sites are likely to be small in most cases, with the greatest concern being maximally exposed individuals rather than the number of cancer cases expected in the exposed population.     

Mixed models for assessing correlation in the presence of replication

The need to assess correlation in settings where multiple measurements are available on each of the variables of interest often arises in environmental science. However, this topic is not covered in introductory statistics texts. Although several ad hoc approaches can be used, they can easily lead to invalid conclusions and to a difficult choice of an appropriate measure of the correlation. Lam et al. approached this problem by using maximum likelihood estimation in cases where the replicate measurements are linked over time, but the method requires specialized software. We reanalyze the data of Lam et al. using PROC MIXED in SAS and show how to obtain the parameter estimates of interest with just a few lines of code. We then extend Lam et al.'s method to settings where the replicate measurements are not linked. Analysis of the unlinked case is illustrated with data from a study designed to assess correlations between indoor and outdoor measurements of benzene concentration in the air.