Estimation of sampling uncertainty for determination of pesticide residues in plant commodities

In order to provide residue data for refining the estimated sampling uncertainty, a coordinated research program was initiated for performing field studies on residues in individual items of leafy vegetables, small and large crops. The trials were carried out in 13 countries with 3 small fruits, 5 large crops, 2 medium/large crops and 3 leafy vegetables. The 25 pesticide active ingredients applied represented the dicarboximide (3), organophosphorus (8), synthetic pyrethroids (5), phthalimides (2), organochlorine (1) and other types of pesticides (6). In addition, 11 supervised field trials were performed in grapes and lettuce by the pesticide manufacturers, and their results were provided for evaluation. The studies represented actual agriculture practice around the world, and provide reliable data for estimation of sampling uncertainty. Based on the 12346 residue data, the best estimate for the relative sampling uncertainty for composite samples, assuming sample size of 10 for small crops and leafy vegetables and 5 for large crops, with 95% confidence limits in brackets are: small commodities: 0.25 (0.20–0.29); Brassica leafy vegetables: 0.20 (0.16–0.24); large commodities: 0.33 (0.29–0.38).     

Characterization of distribution of pesticide residues in crop units

The characteristic features of distribution of pesticide residues in crop units and single sample increments were studied based on more than 19,000 residue concentrations measured in root vegetables, leafy vegetables, small-, medium- and large-size fruits representing 20 different crops and 46 pesticides. Log-normal, gamma and Weibull distributions were found to provide the best fit for the relative frequency distributions of individual residue data sets. The overall best fit was provided by lognormal distribution. The relative standard deviation of residues (CV) in various crops ranged from 15–170%. The 100–120 residue values being in one data set was too small to identify potential effects of various factors such as the chemical and physical properties of pesticides and the nature of crops. Therefore, the average of CV values, obtained from individual data sets, were calculated and considered to be the best estimate for the likely variability of unit crop residues for treated field (CV = 0.8) and market samples (CV = 1.1), respectively. The larger variation of residues in market samples was attributed to the potential mixing of lots and varying proportion of non-detects. The expectable average variability of residues in composited samples can be calculated from the typical values taking into account the sample size.     

Recovery of a marker strain of Salmonella typhimurium in litter and aerosols from isolation rooms containing infected chickens

Abstract

Screening of poultry flocks for foodborne pathogen Salmonella contamination is critical for Salmonella control in preharvest stages of poultry production. In this study, two sampling methods (litter and air filter) were compared for detection of S. typhimurium from experimentally infected chicks some of which had received either a probiotic competitive exclusion culture or transfer of cecal contents from salmonellae‐free adult birds. At 4, 9, and 11 days after inoculation, S. typhimurium samples were enumerated by selective plating. For both types of sampling, the control birds yielded the greatest levels of environmental contamination followed by the samples from the probiotic inoculated birds with the birds receiving the cecal transfer culture having the lowest levels of contamination. Although the two sampling methods responded in a similar fashion, detection sensitivity needs to be increased for air filter sampling.     

Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery1

Claggett, Peter R., Judy A. Okay, and Stephen V. Stehman, 2010. Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery. Journal of the American Water Resources Association (JAWRA) 46(2):334-343. DOI: 10.1111/j.1752-1688.2010.00424.x Abstract: The Chesapeake Bay watershed encompasses 165,760 km² of land area with 464,098 km of rivers and streams. As part of the Chesapeake Bay restoration effort, state and federal partners have committed to restoring 26,000 miles (41,843 km) of riparian forest buffers. Monitoring trends in riparian forest buffers over large areas is necessary to evaluate the efficacy of these restoration efforts. A sampling approach for estimating change in riparian forest cover from 1993/1994 to 2005 was developed and implemented in Anne Arundel County, Maryland, to exemplify a method that could be applied throughout the Bay watershed. All stream reaches in the county were stratified using forest cover change derived from Landsat imagery. A stratified random sample of 219 reaches was selected and forest cover change within the riparian buffer of each sampled reach was interpreted from high-resolution aerial photography. The estimated footprint of gross change in riparian forest cover (i.e., the sum of gross gain and gross loss) for the county was 1.83% (SE = 0.22%). Stratified sampling taking advantage of a priori knowledge of locations of change proved to be a practical and efficient protocol for estimating riparian forest buffer change at the county scale and the protocol would readily extend to much broader scale monitoring.     

Which compounds contribute most to elevated airborne exposure and corresponding health risks in the Western Balkans?

A majority of ongoing monitoring of persistent organic pollutants (POPs) is currently focused on chemicals emphasized in the Stockholm Convention. Quantitative detection of other substances (especially those with numerous anthropogenic sources such as polyaromatic hydrocarbons (PAHs)) is, however, also needed since their concentrations are usually several orders of magnitude higher. A goal of this study was to determine how various groups of compounds contribute to total human health risks at the variety of sampling sites in the region of Western Balkan. Distribution of the risks between the gas and particulate phases was also addressed. Results showed that inhalation exposure to organochlorine pesticides (OCPs) does not represent a significant risk to humans, while polychlorinated biphenyls (PCBs) re-volatilized to the atmosphere from contaminated soils and buildings can pose a problem. PCB evaporation from primary sources (currently used PCB-filled transformers or non-adequate storage facilities) generally resulted in much higher atmospheric concentrations than evaporation from the secondary sources (soils at the sites of war destructions). A majority of the human health risks at the urban sites were associated with PAHs. Between 83 and 94% of the cumulative risk at such sites was assigned to chemicals sorbed to particles, and out of it, PAHs were responsible for 99%.     

KALMAN FILTER CALCULATION OF SAMPLING FREQUENCY WHEN DETERMINING ANNUAL MEAN SOLUTE CONCENTRATIONS

ABSTRACT: A linear filter (Kalman filter) technique was used with a Streamflow-concentration model the minimize surface water quality sampling frequencies when determining annual mean solute concentrations with a predetermined allowable error. The Kalman filter technique used the stream discharge interval as a replacement for the more commonly used time interval. Using filter computations, the measurement error variance was minimized within the sample size constraints. The Kalman filter application proposed here is applicable only under several conditions including: monitoring is solely to estimate annual mean concentration; discharge measurement errors are negligible; the Streamflow-concentration model is valid; and monthly samples reflect the total variance of the solute in question.     

VARIABILITY IN MEASURED BEDLO AD-TRANSPORT RATES1

ABSTRACT: Variability in bedload-transport rates during constant water discharge is an inherent part of the bedload-transport process. Although this variability has been measured extensively in the laboratory, similar information generally is not available from field measurements. During a four-day period of nearly constant water discharge, four sets of consecutively collected bedload samples, ranging from 43 to 120 samples, were obtained at the same cross channel location using a standard 65-pound Helley-Smith bedload sampler. When the measured transport rates are converted to dimensionless rates and plotted as cumulative frequency distributions, they show good agreement with a theoretical probability distribution function of rates derived for the case of ripples on dunes. The distributions show that during constant water discharge individual measured rates at a fixed point vary from near zero to four times the mean rate, and 60 percent of the sampled rates will be less than the mean. Because of the large variation in transport rates that occurs at every location in the cross section, many observations are required to establish an accurate estimate of the mean rate at any given location.     

IDENTIFICATION OF AQUIFER DISPERSIVITIES: METHODS OF ANALYSIS AND PARAMETER UNCERTAINTY1

ABSTRACT: This paper presents a method for estimating aquifer dispersivities in solute transport models. Sensitivity equations are derived for the calculation of sensitivity coefficients. A modified Gauss-Newton algorithm is used to perform the least-squares minimization. A statistical procedure is outlined to assess reliability of the estimated parameters. The solute transport model is solved by the upstream weighted, multiple cell balance method which combines the concepts of local mass balance and finite element approximations. A one-dimensional solute transport problem in a vertical column system is first used to illustrate the inverse technique. A second example considers the parameter identification problem for three-dimensional solute transport with a unidirectional steady and uniform flow field. The third example solves the parameter identification problem in a three-dimensional, stream-aquifer, solute transport system with steady state flow. Numerical experiments are conducted to study data requirements for parameter identification.     

STATISTICAL METHODS AND SAMPLING DESIGN FOR ESTIMATING STEP TRENDS IN SURFACE-WATER QUALITY1

ABSTRACT: This paper addresses two components of the problem of estimating the magnitude of step trends in surface water quality. The first is finding a robust estimator appropriate to the data characteristics expected in water-quality time series. The Hodges-Lehmann class of estimators is found to be robust in comparison to other nonparametric and moment-based estimators. A seasonal Hodges-Lehmann estimator is developed and shown to have desirable properties. Second, the effectiveness of various sampling strategies are examined using Monte Carlo simulation coupled with application of this estimator. The simulation is based on a large set of total phosphorus data from the Potomac River. To assure that the simulated records have realistic properties, the data are modeled in a multiplicative fashion incorporating flow, hysteresis, seasonal, and noise components. The results demonstrate the importance of balancing the length of the two sampling periods and balancing the number of data values between the two periods. The inefficiency of sampling at frequencies much in excess of 12 samples per year is demonstrated. Rotational sampling designs are discussed, and efficient designs, at least for this river and constituent, are shown to involve more than one year of active sampling at frequencies of about 12 per year.