Distribution of pesticide residues within homes in central New York State.

Residues for 17 pesticides were analyzed in 41 households in central New York State that represented farm, rural, and urban houses. Samples were taken in both summer and winter of 2000-2001 from the same households from four locations; family room carpet; adjacent smooth floor; flat tabletop surface; and settled dust collected in a Petri dish on a tabletop. Pesticide residues were analyzed to identity factors that influence both the transport into and the redistribution of pesticides in the indoor environment. Differences were observed between the various pesticides and pesticide classifications relative to location within and between households as well as by season. Variations in the pesticide residues were related to a number of factors. Higher residues were observed in the farm households, particularly in summer, with the highest amount observed for chloropyrifos in carpet (33 microg/m2). For many pesticides, the frequency of detection and the amount of residues were higher in summer, which relates to usage patterns in agriculture and horticulture; however, larger amounts of insecticides such as mecoprop, resmethrin, and tetramethrin were found on flat surfaces in winter, indicating household use and possible redistribution within the home. Distribution patterns suggest that routines within a household may cause high variation in residues; these practices include indoor pets and treatment for fleas and ticks, use of termiticides, and fastidiousness of occupants. Frequency of pesticide detection was highest in carpet for both summer and winter for all households, indicating that carpets hold pesticides over time. Adsorbent fibrous materials such as textiles hold pesticides by macro- and micro-occlusion in their complex structures. Amounts of pesticide residue were higher in carpets than on smooth floors, particularly for rural farm households where the farmer was a certified pesticide applicator. The maximum amount of pesticide residue on a smooth floor surface was 13.6 microg/m2 malathion while the maxima on wiped surfaces and in settled dust were 1.8 microg/m2 2, 4 D and 3 microg/m2 pendimethalin, respectively. Physical properties of individual pesticides such as vapor pressure influenced the distribution of the pesticide within the households. Evidence of volatilization of pesticides and redeposition on surfaces was observed, indicating that this is a mechanism for contamination of surfaces in addition to adsorption on airborne particles and tracking. High residues in winter are evidence that closure of households in winter that reduces ventilation results in redistribution of pesticides within households.

A review of structure-based biodegradation estimation methods.

Biodegradation, being the principal abatement process in the environment, is the most important parameter influencing the toxicity, persistence, and ultimate fate in aquatic and terrestrial ecosystems. Biodegradation of an organic chemical in natural systems may be classified as primary (alteration of molecular integrity), ultimate (complete mineralization; i.e. conversion to inorganic compounds and/or normal metabolic processes), or acceptable (toxicity ameliorated). Most of the biodegradation correlations presented in the literature focus on the characterization of primary or ultimate, aerobic degradation. The US Environmental Protection Agency (USEPA) is charged with determining the risks associated with the thousands of chemicals employed in commerce, an effort that is being facilitated through much research aimed at reliable structure-activity relationships (SAR) to predict biodegradation of chemicals in natural systems. To this end, models are needed to understand the mechanisms of biodegradation, to classify chemicals according to relative biodegradability, and to develop reliable biodegradation estimation methods for new chemicals. Frequently, published correlations associating molecular structure to biodegradation will attempt to quantify the degradability of a limited set of homologous chemicals. These correlations have been dubbed quantitative structure biodegradability relationships (QSBRs). More scarce and valuable to researchers are those models that predict the biodegradability of compounds possessing a wide variety of chemical structures. The latter may use any of several techniques and molecular descriptors to correlate biodegradability: QSBRs, pattern recognition, discriminant analysis, and principle component analysis (PCA), to name several. Generally, models either predict the propensity of a chemical to biodegrade using Boolean-type logic (i.e. whether a chemical will "readily biodegrade" or not), or else they quantify the degree of biodegradation by providing information such as rate constants. Such quantitative predictions of biodegradability come in a diversity of parameters, including half-lives, various biodegradation rates and rates constants, theoretical oxygen demand (ThOD), biological oxygen demand (BOD), and others. In this paper, after describing the advantages and disadvantages of the various biodegradation estimation methods found in the literature, the best models are compared to conclude which provide the greatest utility for determining the biodegradability of chemicals with widely varying structures. The group contribution technique presented by Boethling et al. [Environmen. Sci. Technol. 28 (1994) 459] appears to be the most advantageous for use in broad screening for tendency to biodegrade. The model is simple to use, calculating a probability of biodegrading ranging from 0 (none) to 1 (certain), and has proven to be accurate for a wide range of chemical structures, as established by the large, high-quality data set (BIODEG evaluated biodegradation database, Syracuse Research Corporation, Merrill Lane, Syracuse, NY 13210) used to develop this correlation. The authors, therefore, recommend the method of Boethling et al. [Environ. Sci. Technol. 28 (1994) 459] for the initial screening of chemicals to aid in determining whether additional information is necessary to establish relative biodegradability. For readers with applications requiring more quantitative results, such as biodegradation rate constants, enough model details are presented in this paper to allow the reader to pick a suitable correlation, although the reader is cautioned to consult the original, primary reference for the complete method description, equations, and limitations.