The hydroxide precipitation and coagulation-flocculation methods were used to treat wastewater containing lead, zinc, copper, and iron. The concentrations of heavy metals in the synthetic wastewater range from 1 to 14 mg/L for lead, 5 to 90 mg/L for zinc, 3 to 90 mg/L for copper and 5 to 45 mg/L for iron. Individual Zn(II) and Cu(II) with concentrations below 90 mg/L and Fe(III) with concentrations below 45 mg/L were removed up to 99% by the precipitation method in the pH range of 8.7 to 9.6, 8.1 to 11.1, and 6.2 to 7.1, respectively. Though the highest percent removal of Pb(II) by hydroxide precipitation alone was approximately 98%, the final dissolved concentrations did not fulfill the Standard A discharge limit required by the regulations, thus further treatment by the coagulation-flocculation process was performed. Aluminum sulphate (alum), polyaluminum chloride (PACl) and magnesium chloride (MgCl2) have been used as coagulants together with Koaret PA 3230 as coagulant aid to determine the effectiveness of the coagulation method for the removal of individual heavy metals in the wastewater. The effects of parameters such as pH, type, and dosage of coagulant on the percentage of metal removal, and the amount of coagulant aid on the flocs settling time were investigated. The jar test showed that up to 99% removal of Pb(II) was attained by the addition of 1,200 mg/L of alum, 150 mg/L of PACl, and 2,000 mg/L of MgCl2 in a pH range of 6.5 to 7.8, 8.1 to 8.9, and 9.7 to 10.9, respectively.
While inspection at the buying points focuses on the physical characteristics of the peanuts (Cowart, et al. 2016), our focus in this analysis is to provide an evaluation of other characteristics, specifically the presence, if any, of heavy metal or other chemical residues present in the U.S. peanut crop. Exposure to heavy metals is a major health concern and the consumption of food contaminated with heavy metals has been linked to several adverse health effects. Lead can cause increased blood pressure and kidney damage in adults and can affect the development of the brain and nervous system in children. Consumption of arsenic has been linked to cancers of the skin, lungs, and bladder. Chronic exposure to mercury can lead to damage of the kidneys and nervous system and ingestion of cadmium can affect the kidneys, lungs and bones.
The presence and prevalence of heavy metals has been analyzed for a variety of foodstuffs. The primary source of mercury in the diet is through the consumption of fish and shellfish while the ingestion of cadmium arises mainly from terrestrial foods (Hajeb, et al. 2014). With the exception of rice, the primary source of arsenic is water which can lead to crop contamination via irrigation (Hajeb, et al. 2014). The lead naturally present in plant soils primarily accounts for its entrance into the food system especially in areas with high concentrations (Hajeb, et al. 2014).
Due to the health effects associated with the ingestion of these toxic elements a combination of research and regulation has been used to mitigate their impact. On the research side a number of studies have analyzed the effect of various processing methods to control concentration levels (see Hajeb, et al. 2014 for a thorough review). On the regulatory side, several international bodies have set standards regarding the levels of heavy metals that can be safely ingested. Domestically, U.S. regulation has proceeded on more of a case-by-case basis setting regulatory standards covering specific foods and specific chemicals.
At each buying point, the laboratory samples were chosen during the farmer stock grading process when peanuts are split for internal damage detection. At the end of the day, the FSIS samples used in the grading process were comingled and a randomly drawn 2kg sample was removed for laboratory testing. These samples were sent to the Eurofins laboratory located in New Orleans for pesticide, heavy metal and glyphosate screening. In order to get a representative sample of U.S. peanut production, samples were selected from the three major growing regions, the Southeast (SE), Southwest (SW), and the Virginia, North Carolina, South Carolina (VC) region. Samples included all three major market types: runner (all regions), Virginia (SW and VC), and Spanish (SW only), drawn from the 16 different buying point locations in nine states. The proportion of samples drawn from each region was chosen to represent, as closely as possible, that region's percentage of total peanut acres and market types. Sample availability varied slightly with each crop year based on growing conditions but generally stayed the same across all three years. Table 1 describes the sample selection in more detail. Throughout the statistical analysis it is assumed that the samples analyzed represent random samples of peanut production for each region and for overall U.S. peanut production.
Once at the Eurofins laboratory, each 2kg sample was composited and homogenized. A representative sample was then taken for testing. For heavy metals the analysis was performed on a Perkin Elmer NexION 300D ICP-MS. The sample was prepared via microwave assisted acid digestion (nitric acid + hydrogen peroxide). Digestion was performed to completion. There was no spike recovery within the batch. NIST SRM 1568B and 1515 were analyzed as laboratory control samples. Recoveries for analytes of interest were within 10% of 100. For arsenic, the testing did not differentiate between organic and inorganic forms. Glyphosate testing was analyzed by LC-MS/MS. Fortification of samples were done on the matrix to determine recovery.
Additional pesticide testing was performed with test codes QA16Y and QA16Z, modifications of FDA PAM 304 and the German government S-19 method for pesticide residues in fatty foods. Ground samples are extracted by blending with a blend of acetone and hexane and then solvent-exchanged into a cyclohexane / ethyl acetate mixture. Gel permeation chromatography is performed to remove fat from the extracts and the resulting extract is exchanged into an acetone/hexane mix and analyzed by GC-MS/MS using solvent standard calibration. The calibration includes all analytes tested in the screen and continuing calibration verification standards are analyzed before, between and after all samples. One of the sample matrices in the batch of samples was spiked with the analytes being screened to assess recovery in the specific matrix.
Given the type of censoring present in this data and wanting to impose the least restrictive set of assumptions, the ROS method was used. While the results of all three methods will be reported, these discussions will focus on the ROS results. The analysis presented here focuses on the average residue concentration, estimates and inferences regarding the medians and standard deviations are available from the authors.
The bootstrap method (Efron (1979), Efron and Tibshirani (1986)) is used to obtain an estimate of the sampling distribution of the mean and construct confidence bounds for the statistic without relying on distributional assumptions or approximations. This is especially important for more complicated statistics such as an estimator for the mean when the sample data contains censored observations resulting from detection limits.
The samples used to test for mercury and glyphosate concentrations (as well as some regional samples of arsenic and lead) were so highly censored that none of the above methods would produce reliable results. For these chemicals, the probability that a sample chosen at random will have a concentration that exceeds the detection limit, i.e., the relative frequency with which censored observations occur in the population, is estimated.
While differences in mean concentrations are examined, the exact cause of any differences is beyond the scope of this research. One source of regional differences is likely to arise from differences in background soil levels. For example based on visual inspection of geochemical soil maps (Smith et al. (2014)), one might expect the Southeast region to have lower levels of cadmium, arsenic, and lead since these elements are relatively less prevalent in southeastern soils. Yearly differences within a region could be due to heavy metal concentrations in fertilizers used for rotational crops.
The cadmium samples and statistical analysis are described in Figure 1 and Table 2. Since none of the cadmium samples were censored, the mean values are equal to the arithmetic mean. 95% confidence intervals and 95% upper confidence limits (UCL95) for the population mean are constructed using the bootstrap method.
The frequency distribution of the arsenic test results are described in Figure 2. Due to detection limits, the arsenic test results are censored below a concentration of 0.02 ppm. In order to account for the information contained in the censored observations, the statistical analysis presented in Table 3 employs three different methods; Kaplan-Meier (KM), regression on order statistics (ROS), and maximum likelihood estimation (MLE).
Using the three different methods to account for the censored observations results in different mean concentration estimates. The discussion presented below focuses on the results using the ROS methodology.
In addition to the tests for heavy metals and glyphosate, samples were also tested for a wide array of pesticide residues including pyrethroids, organophosphates, organochlorines, carbamates, dicarboximides, and organonitrogens. All of the test results indicated a concentration below the detection limit of 0.01 ppm.
This analysis has provided evidence regarding the concentrations of chemical residues in the U.S. peanut crop. Table 8 summarizes the estimation results for the sample mean and the UCL95 for cadmium, arsenic, and lead for each crop year and over all crop years. With regard to the concentrations of these heavy metals, no significant statistical differences across crop years were found. 2b1af7f3a8