DRAFT AQUARIUS
Shellfish Sanitation Simulator Rainfall and Water Quality Closure Rule Evaluator
Version 1.0 Fred S Conte, PhD, Extension Aquaculture Specialist Abbas Ahmadi, PhD, Software Engineer Department of Animal Science University of California, Davis, California, USA AQUARIUS SOFTWARE MANUAL
Copyright © 2003 - 2005 Regents of University of California DRAFT ACKNOWLEDGMENTS Aquarius: Shellfish Sanitation Simulator, Rainfall and Water Quality Closure Rule Evaluator was developed by personnel of the Department of Animal Science at the University of California Davis, and a Development committee consisting of personnel from the California Department of Health Service and Coast Seafoods, Inc. Eureka California. Special thanks are extended to the Development Committee, whose input, guidance and cooperation were essential to the project. F. S. Conte and A. Ahmadi DEVELOPMENT COMMITTEE California Department of Health Services Greg Langlois Ray Tom Glenn Takeoka Rolf Frankenbach Marc Commandatore Jack McGerk, Retired Coast Seafoods, Inc Greg Dale, Manager, Eureka facilities F. Robert Studdert, Attorney (1931-2003) 2 TABLE OF CONTENTS
SECTIONS Acknowledgments Index Introduction General Description Shellfish Sanitation Simulator: Rainfall and Water Quality Closure Rule Evaluator Loading Rainfall Data Format of Rainfall Data File Browse the Rainfall Data Format of Fecal Coliform Data File Browse the Fecal Coliform Data Evaluation of Closure Rules Results: Simulations of Events Discussion of Critical Period Statistical Analysis Explanation of Statistical Report Rules for Acceptance or rejection of data Set Analysis References Appendices Page 2 3 4 5 6 6 8 10 12 14 15 17 18 19 20 27 28 29 3 Introduction Aquarius is a software simulation and statistical analytical tool designed to develop or modify rainfall closure rules that affect when filter feeding shellfish may be harvested from conditionally approved growout areas. Rainfall closure rules are established by sanitary surveys taken by Federal and State health service agencies during worst-case scenarios (storm conditions). They are based on measurements of the amount of fecal coliform bacteria washing into the bay, and how long it takes for the bacteria to disappear due to tidal flushing and current. Fecal coliform bacteria are used as indicator organisms for the presence of sewage and animal waste containing potential pathogens contaminating the bay. Filter feeding shellfish can concentrate the indicator organisms and the potential pathogens, the latter which form a health risk. The National Shellfish Sanitation Program (NSSP) (see Appendices) has established bacteriological standards for the classification of shellfish harvest areas. The analysis for fecal coliform takes 24 hours, and numbers of bacteria are expressed in the units of Most Probable Number (MPN) per 100 milliliters (ml). In the establishment of regulations governing harvest area conditions, statistical analyses are used to determine the level of accumulated fecal coliform bacteria as above or below a cutoff point of a most probable number (MPN) of 43. For areas to be classified as Approved, or Conditionally Approved, the level of fecal coliform in sub-surface water samples, at harvest, must meet the "NSSP 14/43" standard. The fecal coliform median or geometric mean must not exceed 14 MPN/100 ml, and the estimated 90th percentile may not exceed 43 MPN/100 ml. Approved growing areas are areas in which the MPN is less than 43 (< 43), even during worst case scenarios (storms). Conditional Approved areas are growing areas that are subject to open and closed harvest periods based on accumulative rainfall. In Conditional Approved areas, data taken during the sanitary survey, and based on the accumulation of fecal coliform following a given level of rainfall, are used to establish closure timeframes for the harvest of shellfish. The area is reopened based on timeframes needed for flushing of the bay, usually by tidal exchange, until fecal coliform levels decline to levels < 43. Because of the expense associated with continuous monitoring of water quality and shellfish meat, conditional growing areas are operated under a set of rainfall closure rules established by the regulatory agencies. The rainfall closure rules usually stay in place until a new sanitary survey is conducted. Sanitary surveys are conducted about ever 10 to 12 years. Both the industry and State and Federal health service agencies would benefit from more frequent analysis of the bay that would be conducted in the interim between sanitary surveys. Often, during the period between full sanitary surveys, conditions in the watershed may improve or degrade with respect to sources of 4 fecal coliform. If the watershed is improved, conditions may be such that a rainfall closure rule would than be too strict and a shellfish company loses income because of excessive harvest closure. If the watershed degrades, a rule may be too lenient, and public health is at risk. However, more frequent full sanitary surveys are almost cost prohibitive, both monetarily and relative to personnel time commitment needed to process and evaluate data. If there is evidence that bay conditions have improved, or degraded between major sanitary surveys, the State regulatory agency may work cooperative with the shellfish industry to obtain water and meat samples to make interim adjustments to the water quality closure rules. Even if the sampling cost are primarily financed by the industry, the restraints to the regulatory agency implementing such interim analysis are the cost in person power to run the multiple statistical analysis necessary to test numerous hypothetical rainfall adjustments and variations in closure periods. To overcome these constraints, personnel from the University of California Davis established a development team consisting of personnel from the California Department of Health Service and a representative of the shellfish industry to develop a tool (Aquarius) that could use shellfish sanitation laboratory and rainfall data to rapidly simulate hypothetical changes to existing rainfall closure rules and run comparative statistical analysis to determine the validity and safety of a new proposed rule. General Description: Aquarius (Version 1.0) is a Windows-based program developed for State and Federal health service agencies, and the commercial shellfish industry. It is designed to evaluate proposed or hypothetical changes in rainfall closure rules based on rainfall and fecal coliform databases maintained by the associated regulatory agency. It is a software simulation program coupled with statistical programs that evaluate potential changes in rainfall closure rules associated with conditional approved shellfish growing areas. Aquarius is designed with an open architecture: meaning that it uses elements of Excel spreadsheet, Access database, Notepad, SAS Statistical Program, and Visual Data Basic Programming. In Version 1.0, archived rainfall databases are in dbf format and can be altered by the agency as new data are added. Rainfall data show cumulative 24-hour rainfall and accommodate unlimited entry. When a rainfall database is loaded for a simulation, it may be viewed in a Browse Mode, but not altered. This allows the responsible agency to protect the integrity of the database when the program is operated in a public forum. Fecal coliform data is maintained and operated under the same conditions, with both archived data that can be protected, but browsed when activated by the software. 5 When performing an analysis, the pertinent rainfall and fecal coliform databases are individually loaded from archived files into the software program before initiating the simulation and statistical analysis. The data input window shows the identification of the region, area, site dates and sample types; and the parameters of the current rule compared against the parameters of a proposed new rule. There are six variables contained in current rainfall closure rule. The hypothetical rainfall closure rule contains the same six variables, each, or all that may be modified in the hypothetical new rule. When activated the program evaluates and calculates the 90th percentile, and performs a simulation of events under application of the existing rule and the proposed new rule. A simulation result window exhibits the total number of days, the number of days open and closed, and the days different between the two rules. Two additional sub-windows compare the new rule with the current rule. They show all the days in the analysis, the critical period when days under the new rule are open; and when the same days are closed under the current rule. Options to view or print the associated statistics are provided. When activated, statistical analyses of the hypothetical scenario are preformed to determine if the data in the critical period supports a new rule change. The View Statistics and Print Statistics buttons initiate a series of statistical analysis, and produces a report that is divided into ten sections. Sections 1 through Section 7 are descriptive statistics, in which the current rule and the proposed new rule are analyzed and contrasted. They provide information about sample numbers, Median, Percent MPN greater than 43, Geometric Mean, Log Average, Log Standard Deviation and Estimated 90th Percentile for each rule when the site is open, and when the current rule is closed and the new rule open. There are check points that signal a termination if the Estimated 90th percentile is greater than MPN 43. Sections 8 through Section 10 are parametric statistics in which T-Test analyses compare (Current Closed - New Open against Current Open), (Current Open New Closed against New Open) and (New Open against Current Open). The final statistical analyses determine whether or not the T-Tests show a significant difference at Alpha = 0.05; and interpretive as whether or not the New Open is equal to the Current Open. If equal, this means that the data support the New Rule to be the same as the Current Rule, and the area may be considered for operation under the New Rule. 6 Shellfish Sanitation Simulator Rainfall and Water Quality Closure Rule Evaluator
When the software is first activated, the Water Quality: Closure Rule Evaluator Input dialog box showing the last settings used, or a new dialog box with no data entries will appear. Close the dialog box. If you do not close this window, the option for loading rain fall data remains grayed out and unavailable. Loading Rainfall Data
To load rainfall data into Aquarius: 1. Choose the "Load Rainfall Data" option from the "File" menu. A dialog box appears: 2. In the Open dialog box, locate the appropriate rainfall data file and click the "Load" button. 3. A rainfall data file is a plain text file with an extension of dat. The program comes with a sample data file called eureka1948-2003.dat, which contains fifty-five years of rainfall data for the Eureka, California area. 7 Note: You can load rainfall data for different regions. The Aquarius program can handle any appropriate rainfall data from any region, internationally. Each time you load data for a region, the existing data for that region will be completely cleared and then the new data will replace the old data. This means that you cannot incrementally load data for various years for one region. The entire data for a region should be in one single .dat file. Format of Rainfall Data File
The rainfall data is an plain text file. Use windows NOTEPAD to edit these files. Never ever use Microsoft Word to modify these files. The Microsoft Word stores a lot of formatting codes making it impossible to read by the program. The Rainfall data file has a special structure. You must follow this special structure. 8 · · The first line of the data file should start with the name of region, for example EUREKA. The program ignores other words on this line. The region should not have any internal space. For example "Humboldt Bay" will be loaded as "Humboldt" and the word Bay will be ignored. To overcome this limitation, type it as "HumboldtBay" without inserting a space. Each line in data file should be 132-characters wide, divided into 33 columns, each 4 digits-wide. The first column is for the Year. For example, 1948. The second column is for the Month. For example, 1PR, 2PR, ..., 12PR, which stands for January, February, ..., December. The third column is for Day 1. The fourth column is for Day 2 The fifth column through the 33rd column is for Day3 through Day 31. Even the months with less than 31 days, should have days 1 through 31. Place the letter M, which means Missing, for those days not in the month. For example, for Day 31 in month = 9PR (September), type the letter M Enter rainfall data after multiplying it by 100. For example a rainfall of 1.20 inch is written as 120. A rainfall of 0.05 inch is written as 5. A rainfall of 0.01 is written as 1. A rainfall of less than 0.01 inch is recorded as the letter T for Trace rain. Trace rain is any rain below 0.01 inch, which is not measured. Make sure to type the heading properly, because the program looks for an equal sign to finds out where the actual data starts. It ignores any extra lines between the Region and the first equal sign. · · · · · · · · · 9 · Following graph shows the right half of the data file: Note: The Aquarius program deletes all missing values; therefore you should enter a rainfall value for each valid date. Otherwise, you will end up having problem when you want to analyze the data. Browse the Loaded Rainfall Data
After you have loaded the rainfall data, you can browse it to make sure everything is correct. To browse the rainfall data follow these steps: 1. Choose the "Browse Rainfall Data" option from the "File" menu. A dialog box appears. 10 Use the scroll bar to browse through the entire rainfall database. It shows Region, Year, Month, Day, Date, Rainfall, and 10-days Cumulative Rainfall. After the rainfall data is imported into the Aquarius system, the data entries are multiplied by 100; and therefore you will see the rainfall values as normal. For example, a rainfall of 305 will be shown as 3.05 inch. The Trace rainfall is exhibited as 0.005 inch. 11 Loading Fecal Coliform Data
To load Fecal Coliform data: 1. Choose the "Load Fecal Coliform Data " option from the "File" menu. A dialog box appears: 2. In the Open dialog box, locate an Excel file, which contains you're the desired Coliform data. For example, fcReal.xls, and click the OK button. 3. The program loads the data into the Aquarius system. Format of Fecal Coliform Data File
Use Microsoft Excel program to store Fecal Coliform data. You can use the Excel program which comes with the Microsoft Office 2000 package. The Excel file should have 10 columns in the following exact order. Any other columns to the right of these columns will be ignored. 12 Region: Width = 30 chars. Format General. No blank space in the name Area: Width = 10 chars. Format General SiteNo: Width = 10 chars. Format General Agency: Width = 10 chars. Format General County: Width = 10 chars. Format General SampleType: Width = 10 chars. Format General Dt: Date: MM/DD/YYYY. Format Date 3/4/2001 Tm: Time: Number with 0 decimal place FcMPN: Fecal Coliform Most Probable Number. Width = 8 digits, decimals=2 FCMOD: Either blank, or less than, or greater than. Width = 1 char. Format General Note: A value, for example of 1.75, in the FCMPN column, and a character of "43, the statistical analysis does not support the proposed change, and the analysis can terminate at this point. In this example, this number is 17.92, therefore, the analysis can continue. Section 4: Critical Period (When New Rule is More Relaxed) Section 4 uses samples taken during the critical period: during those days in which the site was closed under current rule, but would be open under new rule. This section calculates and shows descriptive statistics. The important number is the number on the last line of this sections report, the Estimated 90th Percentile. If this number is >43, the statistical analysis does not support the proposed change, and the analysis can terminate at this point. In this example, the Estimated 90th Percentile is 33.95, which is 43, as it demonstrates that the site was properly closed during this period. In this analysis, the Estimated 90th Percentile is 179.68, which is > 43, and the analyses continue. 23 Section 7: New Close Period Section 7 uses samples taken during those days that the site would be closed under the proposed new rule. The analysis provides descriptive statistics, and the important number is the number on the last line, that of the Estimated 90th Percentile. This value shows that the site should be closed under the new, proposed rule, as demonstrated, when the water is contaminated with coliform levels >43. In this example, the Estimated 90th Percentile is 265.19, which is >43. Note: After completion of Sections 1 through 7 (descriptive statistics) the analysis employs parametric statistics (T-Tests) shown in Sections 8 through 10 to compare aspects of the current rule with the proposed new rule. Section 8: T-Test Comparing Critical Period vs. Current Open 24 Section 8 is the first of the parametric statistical tests. For the proposed rule change to be statistically valid, the analysis requires that there were a sufficient number of samples taken during the Critical Period, when the site is closed under current rule, but opens under new rule (Cur:Close New:Open; n1). There also should be sufficient samples taken during the Current Open (Cur:Open; n2). Samples for Critical Period and Current Open Period are independent from each other. The important values for site interpretation are the three values circled in red. The middle value, Computed t sub c, should fall within the lower and upper limit (between Lower t sub a & Upper t sub b) for the results to be considered positive for a change in the rule. The value 1.894 falls between the range of -2.145 and +2.145. This means that the Critical Period is the same as the Open period under the current rule. If this value is greater than the upper limit (for example, +3.546), then the critical period shows more contamination then the current open period. If this happens, the analysis should terminate at this point. But, if the value is less than the lower range (for example, -3.145), then the Critical Period shows less contamination than the Current rule, and the analysis could continue. (This rarely occurs) In this example, Computed t sub c = 1.894, which is within the lower and upper limits and the analysis continues. Section 9: T-Test Comparing Critical Period vs. New Open Section 9 is similar to the statistical comparison in Section 8, but for cases in which the new, proposed rule is more restrictive than the current rule. In this situation, since the new proposed rule is not more restrictive, but more relaxed, 25 the analysis results in mostly zeros. In this analysis, the results of Section 9 can be ignored, and the analysis continued. Section 10: New Open vs. Current Open Section 10 compares the new, proposed open period versus the current open period to determine if there is a significant difference between the two data sets. Again, this requires that there are a significant number of samples that were taken during the Critical Period (values of n1 and n2). The important values are the three values circled in red. The middle value, Computed t sub c, should fall between the lower and upper limits for the analysis to continue. If this value is greater than the upper limit, then the new, proposed open period shows more contamination then the current open period (example, t sub c = 2.312). If this occurs, then the analysis should terminate at this point. In rare incidences, the value of t sub c is lower than Lower t sub a (for example, t sub c = - 2.436). This would mean that the MPN of the samples taken during the Critical Period were significantly lower than the samples occurring in the Current Open, and the new, proposed rule could be accepted. Final Assessment In this analysis, t sub c = 0.422, which falls between the lower and upper limits. The T-Test IS NOT significant at Alpha = 0.05. This means that the New Open Is Equal To the Current Open. In this example, the data meets the statistical criteria for acceptance of the proposed new rule. It shows the proposed, new rule as being equal to the to the current rule, and the proposed, new rule could be accepted as the operational rule for the area. 26 RULES FOR ACCEPTANCE OR REJECTION OF DATA SET ANALYSIS The new proposed, hypothetical closure rule is rejected if any of the following five conditions are NOT satisfied: I. The Critical Period, sample size is at least 10 percent of the total sample size. II. The growing site complies with the "NSSP 14/43" standard when it is open under the new, proposed closure rule. That is, the fecal coliform median or geometric mean must not exceed 14 MPN/100 ml, and the estimated 90th percentile may not exceed 43 MPN/100 ml. III. The growing site complies with the "NSSP 14/43" standard during the Critical Period. IV. The fecal coliform level of "Open under Current Rule" versus "Open Under New Rule" is NOT statistically significant using the T-test method. V. The fecal coliform level of "Open under Current Rule" and "Open during Critical Period" is NOT statistically significant using the T-test method. NOTES The first, second, and third conditions show if the new closure rule complies with the "NSSP 14/43" standard. The fourth and fifth conditions show if the level of fecal coliform under the new proposed rule is NOT statistically significantly different from the level of fecal coliform under the current rule. If all five conditions are met, then the current closure rule may be replaced by the new, proposed closure rule. 27 REFERENCES Clem, David. 1994. Historical Overview. In: Environmental Indicators and Shellfish Safety. Eds. C.R. Hackney and M.D. Pierson. Chapman and Hall, New York. pp. 1-29. Conte, F.S. and A. Ahmadi. 2005. AQUARIUS: A computer program for water quality closure rule evaluation and shellfish sanitation simulation. 2005 EFITA/WCCA Joint Congress on IT in agriculture. 12 p. Interstate Shellfish Sanitation Conference. 2002. NSSP Guide for the Control of Molluscan Shellfish. ISSC, 209 Dawson Road, Suite 2, Columbia, South Carolina, 29223. 28 APPENDIX 1 INTRODUCTION (Modified from NSSP Guide for the Control of Molluscan Shellfish, 2002; ISSC Model Ordinance, 2002) National Shellfish Sanitation Program: In the late 19th and early 20th century, serious illnesses associated with the consumption of raw oysters, clams, and mussels became a national concern in the United States. In response, the Surgeon General of the United States Public Health Service was requested to develop necessary control measures to ensure that shellfish were a safe product for the consumer. In 1924, a conference was held in Washington D.C., which led to the development of the National Shellfish Sanitation Program (NSSP). Today the NSSP is a joint responsibility of Federal and State health service agencies. Since 1925, the NSSP has grow beyond the original objective of insuring that shellfish shipped interstate would not be the cause of communicable disease. Protective measures now address any potential shellfish hazard to public health, including paralytic shellfish poison, contamination from radionuclides, or other poisonous and deleterious substances such as metals, pesticides, and hydrocarbons. As a part of the NSSP program, States developed the legal authority to supervise the growing, harvesting, relaying and transportation of shellfish, and to take immediate emergency action to halt harvesting and processing of shellfish. In essence, the NSSP is the federal/state cooperative program that is recognized by the U.S. Food and Drug Administration (FDA). In the 1980s, a second organization the Interstate Shellfish Sanitation Conference (ISSC) was initiated to increase State and Federal communication pertaining to changing and proposed changes in shellfish regulations. In 1984, the FDA entered into a Memorandum of Understanding with the ISSC recognizing the ISSC as the primary voluntary national organization of State shellfish regulatory officials that provides guidance and counsel on matters for the sanitary control of shellfish. ISSC membership provides a formal structure for State regulatory authorities to participate in establishing updated regulatory guidelines and procedures for uniform state application of the NSSP. The ISSC consists of agencies from shellfish producing and receiving States, FDA, the shellfish industry, the National Marine Fisheries Service, and the U.S. Environmental Protection Agency. The ISSC provides the formal structure wherein State regulatory authorities, with FDA concurrence, can establish updated guidelines and procedures for sanitary control of the shellfish industry. The ISSC operates under formal procedures for state representative to review shellfish sanction issues and develop regulatory guidelines. Upon FDA concurrence, guidelines are published in revision of the NSSP Model Ordinance. The Model Ordinance contains supporting guidance documents, recommended forms, and other related materials associated with the NSSP. Under international agreements with FDA, foreign governments importing shellfish into the United 29 States also participate in the NSSP. Other components of the NSSP include program guidelines, State growing area classification and dealer certification programs, and FDA evaluation of State program elements. 30 APPENDIX 2 ESTIMATING THE NINETIETH PERCENTILE (Guidance Document) 31 32 33 APPENDIX 3 National Shellfish Sanitation Program
U.S. Food and Drug Administration Shellfish Safety Team Division of Cooperative Programs Office of Field Programs Interpretation Number: 99-IV@.02-101 Date: November 27, 2001 Final: May 17, 2002 Model Ordinance Reference: Chapter IV @.02 D, E, F and G Key Words: Weighted 90 Percentile, Adverse Pollution Condition, Systematic th Random Sampling, Estimated 90 Percentile Question: What is the procedure for determining the value of the 90 percentile to be used in the analysis of sample data derived in the transition to a different MPN procedure? Interpretation: A weighted 90 percentile value is calculated for each set of samples derived in the transition to a different MPN procedure. Rationale: A number of states have availed themselves of the advantages th afforded by the action of the 8 National Shellfish Sanitation Workshop in allowing the use of a virtual limitless combination of tubes and dilutions in MPN procedures used in support of the National Shellfish Sanitation Program (NSSP). A change in the combination of tubes and/or dilutions from those traditionally used in the NSSP alters the th precision or variability of the test and thus its associated 90 percentile. When a change in MPN procedures is instituted, th new data with a different 90 percentile must be phased into the existing sample database. During this phase-in period a
th th th 34 "hybrid" 90 percentile value must be calculated and used as the variability component of the bacteriological standard against which the variability of sample data is to be th compared. This "hybrid" 90 percentile value is calculated by weighting the relative contributions of each MPN method to the sample database. The resulting value is known as the th th weighted 90 percentile. Weighted 90 percentile values can be used equally effectively with either Adverse Pollution Condition (APC) or Systematic Random Sampling (SRS) regimes. Calculations: The value of the weighted 90 percentile from a data set derived in the transition to a different MPN procedure is calculated in the following manner: a. Convert the 90 percentile values for both MPN procedures to their respective base 10 logarithmic values.
th th th Interpretation Number: 99-IV@.02-101 Date: 11/27/2001 __________________________________Final:_ 5/17/ 2002 _________________ b. Multiply the logarithmic values for each MPN procedure by the number of samples in the database examined by that procedure. c. Add these logarithmic values, then divide by the total number of samples examined. d. Take the antilog of this value. e. Round off conventionally to the nearest whole number. th f. This value is the weighted 90 percentile against which sample data is compared. th g. Recalculate the weighted 90 percentile when new data is added to the database. h. Once all accumulated data is from the same MPN procedure and the transition in th methodologies is complete, the corresponding 90 percentile value for this MPN procedure is then used for comparing sample data. Example 1 Data was gathered for a sampling station under the APC sampling regime. The growing area which encompasses this sampling station is in the approved classification. The first ten samples in the database were examined by the traditional 5-tube, decimal dilution MPN test for fecal coliforms. The remaining five samples required under APC sampling were analyzed by the 12tube, single dilution MPN test th for fecal coliforms. The 90 percentile value for the 5-tube, decimal dilution MPN th test for fecal coliforms is 43. The 90 percentile value for the 12-tube, single dilution th MPN test is 28. The weighted 90 percentile value which results from this data will lie 35 somewhere between the 90 percentile values of the MPN procedures used. Its th proximity to either method's 90 percentile value will depend on the relative number of samples analyzed from each method. Since most of the samples in this example th were derived from the 5-tube MPN test, the 90 percentile value calculated will be weighted toward 43. To calculate the weighted 90 percentile for this data set: a. The 90 percentile values of 43 for the 5-tube, decimal dilution MPN test and 28 for the 12tube, single dilution MPN test are converted to base 10 logarithms. This gives base 10 log values of 1.633 and 1.447 respectively. b. The base 10 log values are then multiplied by the number of samples in the database examined by each MPN procedure used. Ten of 15 samples were analyzed by the 5-tube, decimal dilution MPN test. The remaining 5 of 15 were examined by the 12-tube, single dilution test. This gives 1.633 for the 5-tube test x 10 samples = 16.330 and 1.447 for the 12tube, single dilution test x 5 samples = 7.235. c. These values are added together and the resultant divided by the total number of samples in the database being used. Thus, 16.330 + 7.235 = 23.565, 23.565/15 =1.571.
th th th Interpretation Number: 99-IV@.02-101 Date: 11/27/2001 Final: 5/17/2002 d. The antilog of this value is taken. In this example, the antilog of 1.571 is 37.239. e. The antilog value is rounded off to the nearest whole number which in this example is 37. th th f. The weighted 90 percentile for this data set is 37. Thirty-seven (37) is the 90 percentile value which cannot be exceeded more than 10% of the time by the sample station data in this data set under the APC sampling regime for this station to remain in the approved classification status. When new data is added to the database of this th sampling station, the value of the weighted 90 percentile would have to be recalculated until the transition in methodologies is completed and all the data from this sampling th station is derived from the same MPN procedure. At this time, the corresponding 90 percentile value of 28 for the 12tube, single dilution MPN procedure in use will be employed in comparisons with sample data. Example 2 Data was derived from a sampling station under the SRS sampling regime. The growing area which encompasses this sampling station is also in the approved classification for fecal coliforms. The first 18 of 30 samples were analyzed using the 3-tube, decimal dilution MPN test. The remaining 12 of 30 samples were examined th using a 12-tube, single dilution MPN test. The 90 percentile values for the 3-tube, decimal dilution test in the approved classification status is 49. That for the 12-tube, 36 single dilution MPN test is 28. Again the value for the weighted 90 percentile will be th somewhere between the respective 90 percentile values of both MPN methods. Its proximity to either is a function of the number of samples in the data set contributed by each MPN procedure. In this example, a somewhat greater number of samples were derived from use of the 3-tube, decimal dilution MPN test; so that, the value of th the 90 percentile will be weighted in that direction also. To calculate the weighted 90 percentile for this data set a. The 90 percentile values of 49 for the 3-tube, decimal dilution MPN test for fecal coliforms and 28 for the 12-tube, single dilution MPN test for fecal coliforms are converted to base 10 logs. This gives base 10 log values of 1.690 for the 3-tube, decimal dilution test and 1.447 for the 12-tube, single dilution MPN test. b. These base 10 log values are then multiplied by the number of samples in the database analyzed by each MPN procedure. In this example, 18 of 30 samples were examined by the 3tube, decimal dilution MPN test: and, 12 of 30 samples were analyzed by the 12tube, single dilution MPN test. This gives 1.690 for the 3-tube, decimal dilution MPN test x 18 samples = 30.420 and 1.447 for the 12-tube, single dilution MPN test x 12 samples = 17.364. c. These values are added together and the resultant divided by the total number of samples in the database being used. Thus, 30.420 + 17.364 = 47.784, 47.784/30 = 1.593. d. The antilog of this value is determined. In this example, the antilog of 1.593 is 39.174.
th th th Interpretation Number: 99-IV@.02-101 Date: 11/27/2001 Final: 5/17/2002 e. This antilog value is rounded to the nearest whole number which in this example is 39. th f. The weighted 90 percentile value for this data set is 39. Thirty-nine (39) is the value of th th the 90 percentile which will be compared to the estimated 90 percentile calculated from the data in the sample data set collected under the SRS sampling regime and examined th using the two different MPN methods. To remain in the approved status the estimated 90 percentile calculated from this data set must be less than or equal to the value determined th th for the weighted 90 percentile of the data set. Again the weighted 90 percentile will have to be recalculated as new data becomes available. This recalculation must continue until the transition in methodologies is completed and all the data from this sampling station has been derived from the same MPN procedure. At this time, the corresponding th 90 percentile of 28 for the 12-tube, single dilution MPN procedure in use will be th employed in comparisons to the estimated 90 percentiles calculated directly from the sampling data. Example 3 Data in this example was collected from a sampling station under the SRS sampling 37 regime. This sampling station is in an area classified as restricted. The first 24 of the 30 samples collected were analyzed by the 5-tube, decimal dilution MPN test for fecal coliforms. The remaining 6 samples of the 30 collected were analyzed using a 5-tube, th fivefold dilution MPN test for fecal coliforms. The 90 percentile value for each of th these MPN procedures is 260 and 190 respectively. The value of the weighted 90 percentile for this data set will be somewhere between 190 and 260. The proximity to either value will depend on the respective number of samples analyzed by each MPN method. In this example, most of the samples were derived from the 5-tube, decimal th dilution MPN test. Consequently, the 90 percentile value will be heavily weighted in that direction. To calculate the weighted 90 percentile for this data set: a. The 90 percentile values of 260 for the 5-tube, decimal dilution MPN test for fecal coliforms and 190 for the 5-tube, fivefold dilution MPN test for fecal coliforms are converted to base 10 logs. This gives a base 10 logarithmic value of 2.415 for the 5-tube, decimal dilution MPN test and 2.279 for the 5-tube, fivefold MPN test. b. These base 10 log values are then multiplied by the number of samples in the database analyzed by each MPN procedure. In this example, the 5-tube, decimal dilution MPN was used in the analysis of 24 of the 30 samples while the 5-tube, fivefold dilution MPN was th used to test the remaining 6 samples. Hence, 2.415, the log 90 percentile value for the 5tube, decimal dilution MPN test is multiplied by 24, the number of samples tested by this th MPN procedure to give 57.960; and, 2.279, the log 90 percentile value for the 5-tube, fivefold dilution MPN test is multiplied by 6, the number of samples obtained using this MPN procedure to give 13.674. c. These values are added together and subsequently divided by the total number of samples analyzed by both methods. In this example, 57.960 + 13.674 = 71.634, 71.634/30 = 2.388. d. The antilog of this value is determined. In this example the antilog of 2.388 is 244.343.
th th Interpretation Number: 99-IV@.02-101 Date: 11/27/2001 Final:_5/17/2002 e. This antilog is conventionally rounded to the nearest whole number which in this example is 244. f. The weighted 90 percentile value for the data set is 244. Two hundred forty-four (244) th th is the value of the 90 percentile which will be compared to the estimated 90 percentile calculated from the data in the sample data set collected under the SRS sampling regime and examined using the two MPN methods. To remain in the restricted classification, the th estimated 90 percentile calculated from the data set will have to be less than or equal to th th the value of the weighted 90 percentile obtained from the data set. This weighted 90 percentile value will need to be recalculated as more data becomes available and until such time as the transition in methodologies is completed and all the samples have been
th 38 derived from the same MPN procedure. When this occurs, the corresponding 90 percentile of 190 for the 5-tube, fivefold dilution MPN procedure in use will be employed th in comparisons to the estimated 90 percentile calculated directly from the sampling data. Example 4 Data in this example was collected from a sampling station under the APC sampling regime. This sampling station is in the approved classification and 5 of 15 samples in the database were tested by the 5-tube, decimal dilution MPN test for total coliforms. The remaining 10 samples in the database were analyzed by the 3-tube, decimal dilution MPN th test for total coliforms. The 90 percentile value for each of these MPN tests were 230 th and 330 respectively. The value of the weighted 90 percentile will be somewhere between 230 and 330. Its proximity to either value depends on the respective number of samples analyzed by each MPN procedure. In this example, the preponderance of th samples were tested by the 3-tube MPN procedure. As a result, the value of the 90 percentile will be weighted more heavily toward 330 To calculate the weighted 90 percentile for this data: a. The 90 percentile values of 230 for the 5-tube, decimal dilution MPN test for total coliforms and 330 for the 3-tube, decimal dilution MPN test for total coliforms are converted to base 10 logarithms. This gives base 10 log values of 2.362 a