VARIATION OF ENDOSULFAN RESIDUES IN WATER AND SEDIMENT TAKEN FROM THE MOSS LANDING DRAINAGE OF MONTEREY COUNTY
Data from previous studies conducted by the State Mussel Watch Program (SMW) implied increasing endosulfan residues inmollusks used as indicator organisms of chemical contamination in the Moss Landing Drainage area of Monterey County. As a first step in confirming a chronological trend, studies were conducted by the California Departments of Food and Agriculture (CDFA) and Fish and Game (CDFG) to determine within-site variability of endosulfan concentrations, and to estimate sample size necessary for future research. Personnel from the Environmental Hazards Assessment Program (EHAP) of CDFA collected sediment and water samples while personnel from the Pesticide Investigations Unit (PIU) of CDFG collected mollusk and fish samples at the same sites in a coordinated effort. This report contains results from the EHAP study. Three sites previously monitored by SMW in Monterey County were selected for the collection of sediment and water samples. All samples were analyzed for endosulfan I, II and sulfate. Only 33, 33 and 25% of the sediment samples contained detectable residues of endosulfan I, II and sulfate, respectively. Endosulfan concentrations in sediment were not uniformly distributed in the Moss Landing Drainage area with residues occurring more frequently in agricultural areas, away from tidal action and dredging activities. Only 25% of the water samples contained detectable endosulfan residues in the form of endosulfan sulfate. However, it is conceivable that endosulfan residues exist in water (either dissolved or on suspended sediment) below the detection limit given the consistent find of residues in mollusks and fish in this area and the established bio-concentration factors for fish species. Minimum sample sizes of 22 and 11 for sediment and water, respectively, were estimated as necessary to calculate the true mean for a given site. Future study of temporal or spatial trends in endosulfan residues of this drainage area might best be concentrated on sediment and biological organisms since residues in water are mostly below the current detection limits.
THE INEVITABILITY OF VARIABILITY IN PESTICIDE RESIDUE ANALYSIS
Abstract – All scientific measurements are inherently variable. In analytical chemistry, usually the lower the concentration sought, the greater the variability. There are four major sources of variability in the analytical sequence: sampling, method, analyst, and misinterpretation of the results of the analysis. In general, the major source of variability in pesticide residue analysis is from sampling, which may contribute a relative error, in terms of coefficient of variation, of 20% or more. Sampling error can be reduced only by collecting and analyzing more and larger samples. Many of the commonly used pesticide residue analytical methods used at tolerance levels are equivalent in reliability; thus a choice among them must be based upon such qualitative factors as applicability to a wide range of pesticides and commodities, speed and cost, and availability of reagents and equipment. The coefficient of variation due to methods (from interlaboratory collaborative study data) is about 15%, which when combined statistically with the sampling error, can produce a total “normal” error in pesticide residue analysis of about 25%. Control of the analyst variability, which is included in the sampling and method estimates, requires the use of a continuous program of quality assurance. Errors from the misinterpretation of the results of analysis are controllable by intelligent laboratory administration.
Benchmark database for fine-grained image classification of benthic macroinvertebrates
Abstract Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.