Comparison of regional air dispersion simulation and ambient air monitoring data for the soil fumigant 1,3-dichloropropene.

SOFEA v2.0 is an air dispersion modeling tool used to predict acute and chronic pesticide concentrations in air for large air sheds resulting from agronomic practices. A 1,3-dichloropropene (1,3-D) air monitoring study in high use townships in Merced County, CA, logged 3-day average air concentrations at nine locations over a 14.5month period. SOFEA, using weather data measured at the site, and using a historical CDPR regulatory assumption of a constant 320m mixing height, predicted the general pattern and correct order of magnitude for 1,3-D air concentrations as a function of time, but failed to estimate the highest observed 1,3-D concentrations of the monitoring study. A time series and statistical comparison of the measured and modeled data indicated that the model underestimated 1,3-D concentrations during calm periods (wind speed <1m/s), such that the annual average concentration was under predicted by approximately 4.7-fold, and the variability was not representative of the measured data. Calm periods are associated with low mixing heights (MHs) and are more prevalent in the Central Valley of CA during the winter months, and thus the assumption of a constant 320m mixing height is not appropriate. An algorithm was developed to calculate the MH using the air temperature in the weather file when the wind speed was <1m/s. When the model was run using the revised MHs, the average of the modeled 1,3-D concentration Probability Distribution Function (PDF) was within 5% of the measured PDF, and the variability in modeled concentrations more closely matched the measured dataset. Use of the PCRAMMET processed weather data from the site (including PCRAMMET MH) resulted in the global annual average concentration within 2-fold of measured data. Receptor density was also found to have an effect on the modeled 1,3-D concentration PDF, and a 50×50 receptor grid in the nine township domain captured the measured 1,3-D concentration distribution much better than a 3×3 receptor grid (i.e., simulated receptors at the nine monitoring locations). Comparison of the monitored and simulated PDF for 72-h 1,3-D concentrations indicated that SOFEA slightly over predicts the 1,3-D concentration distribution at all percentiles below the 99th with slight under prediction of the 99-100th percentile values. This suggests that without further refinement, the SOFEA2 model, based upon field validation observations, will result in representative but conservative estimates of lifetime exposure to 1,3-D for bystanders in 1,3-D use areas.

Implications of estimates of residential organophosphate exposure from dialkylphosphates (DAPs) and their relevance to risk.

Recent epidemiological studies have claimed to associate a variety of toxicological effects of organophosphorus insecticides (OPs) and residential OP exposure based on the dialkyl phosphates (DAPs; metabolic and environmental breakdown products of OPs) levels in the urine of pregnant females. A key premise in those epidemiology studies was that the level of urinary DAPs was directly related to the level of parent OP exposure. Specific chemical biomarkers and DAPs representing absorbed dose of OPs are invaluable to reconstruct human exposures in prospective occupational studies and even in non-occupational studies when exposure to a specific OP can be described. However, measurement of those detoxification products in urine without specific knowledge of insecticide exposure is insufficient to establish OP insecticide exposure. DAPs have high oral bioavailability and are ubiquitously present in produce at concentrations several-fold greater than parent OPs. Studies relying on DAPs as an indicator of OP exposure that lack credible information on proximate OP exposure are simply measuring DAP exposure and misattributing OP exposure.

Factors affecting the accuracy of high resolution electron backscatter diffraction when using simulated patterns.

High resolution EBSD directly compares electron backscattering patterns (EBSPs), generated in a scanning electron microscope, to measure relative strain and rotation to a precision of ∼ 10(-4) in strain and 10(-4)rad (0.006 °) in rotation. However the measurement of absolute strain and rotation requires reference EBSPs of known strain and orientation (or a far field region of known strain). Recent suggestions of using simulated EBSPs with known strain show much promise. However precise measurement of the experimental geometry (pattern centre) is required. Common uncertainties of 0.5% in pattern centre result in uncertainty of ∼ 10(-3) in strain state. Aberrations in the compact lenses used for EBSP capture can also result in image shifts that correspond to strains/rotations of ± 10(-3) between experimental and simulated EBSPs. Simulated EBSPs can be generated using dynamical or kinematic models (or a combination of the two). The choice in simulation model has a significant effect on the measured shifts, particularly at zone axis and high structure factor bands, due to large intensity variations, and for simple kinematic simulations can result in the measurement of rogue shifts and thus erroneous strain measurements. Calibrant samples of known strain provide a method of measuring the experimental geometry but imprecise stage movement combined with the high depth of field in the SEM could also result in uncertainties in strain of ∼ 10(-3).

Recrystallization of plane strain compressed Al-1 wt.% Mn alloy single crystals of typical unstable orientations.

A systematic study of crystal lattice reorientation in early stages of recrystallization has been carried out to correlate the orientations of recrystallization nuclei with the deformation microtexture and with slip systems. Microstructure and texture of Al-1 wt.% Mn single crystals of unstable initial orientations of {112}111, {100}001 and {001}110 have been examined by high-resolution field-emission gun scanning electron microscope local orientation measurements. All single crystals were channel-die deformed at room temperature and then annealed for a short time. It was shown that often observed presence of the 112 directions as rotation axes in the formation of new nuclei orientation directly suggested a close link with the deformation process.

Scanning electron microscopy and transmission electron microscopy in situ studies of grain boundary migration in cold-deformed aluminium bicrystals.

The crystallography of recrystallization has been investigated in channel-die deformed pure aluminium bicrystals with {100}<011>/{110}<001> orientations. The microstructural and microtextural changes during the early stages of recrystallization were followed by systematic local orientation measurements using scanning and transmission electron microscopes. In particular, orientation mapping combined with in situ sample heating was used to investigate the formation and growth of new grains at very early stages of recrystallization. Grain boundary migration and 'consumption' of the as-deformed areas was always favoured along directions parallel to the traces of the {111} slip planes that had been most active during deformation.

Pesticide exposure monitoring databases in applied risk analysis.

Faced with the need to evaluate under what conditions chemicals can be used with "reasonable certainty of no harm" to workers and consumers, industry and government agencies have embraced quantitative risk analysis as a science-based approach for product development, regulatory evaluations, and associated risk management decision making. Beginning in the 1990s, a variety of industry-sponsored task forces have been formed to develop exposure-related data to support safety evaluations for pesticide chemicals used in agricultural, industrial, institutional, residential, and other settings. Human exposure assessment and the underlying data (e.g., personal exposure and biological monitoring measurements, media-specific residue measurements, product use, and time-activity information) represent a critical component of the risk assessment process and a rapidly advancing science. While task forces have been created to develop databases for supporting the continued safe use of products, the development of these databases has served to advance general understanding of the basic principles underlying exposure assessment methodology and thereby provide the basis for improved science-based risk management by both industry and government. Given that developing chemical-specific data for every product use pattern and associated worker or consumer exposure scenario (e.g., professional mixer, loader and applicator activities associated with the use of a low-pressure sprayer, consumer residential lawn application via a ready-to-use hose-end sprayer product) is prohibitively expensive and time consuming, alternative approaches have been developed based upon meta-analyses and generalizations derived from databases of exposure monitoring studies for multiple chemicals, sorted by significant exposure covariates such as formulation type, method of application, amount of active ingredient applied, site of application, protective equipment and clothing, and task or activity. These generalizations can be used for predictive exposure analyses and have clearly demonstrated the value of "generic databases." Although data in these databases and associated generalizations are subject to interpretation, e.g., during the regulatory decision-making processes, and may be used in conjunction with additional considerations or assessment methods that result in conservative biases, the role of generic databases for risk management decision making, and advancing the science of applied exposure analysis continues to be realized.

Could pesticide toxicology studies be more relevant to occupational risk assessment?

Pesticide toxicology study design has evolved from concern for oral exposure via food residues. The emphasis on the oral route does not generally apply to workers that are exposed primarily via the dermal route either handling pesticides or re-entering treated fields. As a result numerous assumptions about how oral toxicology results relate to dermal exposure must be made when conducting worker risk assessments. These assumptions introduce a high degree of uncertainty. Alternative toxicology study designs are suggested to reduce uncertainty when assessing risk. Because the dermal route is so important to characterizing occupational risk, methods to improve the accuracy of dermal absorption estimates are suggested, including the use of human subjects to study dermal absorption. Additional suggestions include tailoring dermal, oral and inhalation kinetic study designs to reflect worker exposure dosages. Suggestions are made to routinely conduct a single dose toxicity study patterned after the neurotoxicity study design to distinguish single dose effects and NOAELs from those resulting from multiple doses. Finally, interspecies pharmacokinetics studies are proposed to determine which toxicology study regimen of dosing best reflects intermittent worker exposure.

Estimation of dietary exposure to chemicals: a case study illustrating methods of distributional analyses for food consumption data.

There are a number of sources of variability in food consumption patterns and residue levels of a particular chemical (e.g., pesticide, food additive) in commodities that lead to an expected high level of variability in dietary exposures across a population. This paper focuses on examples of consumption pattern survey data for specific commodities, namely that for wine and grape juice, and demonstrates how such data might be analyzed in preparation for performing stochastic analyses of dietary exposure. Data from the NIAAA/NHIS wine consumption survey were subset for gender and age group and, with matched body weight data from the survey database, were used to define empirically-based percentile estimates for wine intake (microliter wine/kg body weight) for the strata of interest. The data for these two subpopulations were analyzed to estimate 14-day consumption distributional statistics and distributions for only those days on which wine was consumed. Data subsets for all wine-consuming adults and wine-consuming females ages 18 through 45, were determined to fit a lognormal distribution (R2 = 0.99 for both datasets). Market share data were incorporated into estimation of chronic exposures to hypothetical chemical residues in imported table wine. As a separate example, treatment of grape juice consumption data for females, ages 18-40, as a simple lognormal distribution resulted in a significant underestimation of intake, and thus exposure, because the actual distribution is a mixture (i.e., multiple subpopulations of grape juice consumers exist in the parent distribution). Thus, deriving dietary intake statistics from food consumption survey data requires careful analysis of the underlying empirical distributions.

Human exposure assessment. I: Understanding the uncertainties.

Exposure estimates produced using predictive exposure assessment methods are associated with a number of uncertainties that relate to the inherent variability of the values for a given input parameter (e.g., body weight, ingestion rate, inhalation rate) and to unknowns concerning the representativeness of the assumptions and methods used. Despite recent or ongoing consensus-building efforts that have made significant strides forward in promoting consistency in methodologies and parameter default values, the potential variability in the output exposure estimates has not been adequately addressed from a quantitative aspect. This is exemplified by remaining tendencies within federal and state agencies to use worst-case approaches for exposure assessment. In this study, range-sensitivity and Monte Carlo analyses were performed on several different exposure scenarios in order to illustrate the impact of the variability in input parameters on the total variability of the exposure output. The results of this study indicate that the variability associated with the example scenarios range up to more than four orders of magnitude when just some of the parameters are allowed to vary. Comparison of exposure estimates obtained using Monte Carlo simulations (in which selected parameters were allowed to vary over their observed ranges) to exposure estimates obtained using standard parameter default assumptions demonstrate that a default value approach can produce an exposure estimate that exceeds the 95th percentile exposure in an exposed population.

Human exposure assessment. II: Quantifying and reducing the uncertainties.

Alternative methods of human exposure assessment that reduce and/or allow quantification of the uncertainties associated with exposure estimates are surveyed and illustrated. These alternative approaches include (1) use of more appropriate exposure parameter default values rather than values that result in extreme exposure estimates; (2) incorporation of time-activity data to better define appropriate exposure duration values; (3) the use of reasonable exposure scenarios rather than the traditional Maximally Exposed Individual (MEI) approach; (4) the use of stochastic approaches such as Monte Carlo-based and information analysis-based methods; (5) use of bivariate analysis to identify the extent to which interdependencies between different exposure parameters affect the distribution of exposure estimates; (6) use of less-than-lifetime exposure and risk assessment; and (7) incorporation of physiological considerations relevant to absorbed dose estimation, including route-specific impacts, use of improved absorption factors, and application of pharmacokinetic models. Other ways to improve the exposure assessment process, including assuring statistical equivalency in comparing different exposure estimates and incorporation of sensitive subpopulation considerations are also discussed, as are key research needs.

Methods for comparing Salmonella mutagenicity data sets using nonlinear models.

A variety of linear and nonlinear mathematical models have been proposed to characterize Salmonella mutagenicity data sets, but no systematic procedure has been suggested for comparing two or more data sets across experiments, laboratories, occasions, mutagens or treatment conditions. In this paper, a general method for data-set comparison is provided. Nonlinear regression techniques are applied to real data sets. Data-set and parameter equivalence are described in depth. Confidence-band construction for nonlinear models and other graphical techniques are presented as auxiliary tools. Key Statistical Analysis System (SAS) code programs are provided.