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«Final Report of the Cold Compost Project Prepared by The Cornell Waste Management Institute Ithaca, NY Ellen Z. Harrison Director May 2004 Cold ...»

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Microorganisms of Interest Indicator Organisms It is impractical to detect and enumerate all pathogenic organisms of concern. In assessing hygienic quality, typically certain microbes are selected to serve as “indicator organisms.” The assumption is made that if the indicator organism is absent or present in sufficiently low levels, that other pathogenic organisms will also be reduced to acceptable levels. To be a good indicator of compost hygienic quality, the microbe must be present in the initial stages, it must be suitable for analysis using the appropriate methods, and it should be among the hardiest of the pathogens (Prescott et al. 1996).

In this project, several coliform bacteria and fecal Streptococcus were chosen as indicator organisms. Coliforms are part of the Enterobacteriaceae family, which includes Escherichia coli, Enterbacter aerogens, and Klebsiella pneumoniae. Coliforms represent about 10% of the intestinal microorganisms in the human gut. Defined as “facultative anaerobic, gram-negative, non-spore forming, rod-shaped bacteria that ferment lactose within 48 hours at 35°C,” coliforms are widely used as indicator organisms because they are more resistant to desiccation than other microbes found in human and animal digestive systems (Prescott et al. 1996). No indicator is perfect and one study showed that E. coli survived longer in outdoor soil than Streptococcus faecalis during summer, while in spring and winter the fecal strep survived much longer (Donsel et al 1967). This makes the use of E. coli as an indicator questionable.

Fecal coliform are a sub-group of total coliforms (see Figure 1). Total coliform counts often include organisms that do not reside in the intestinal tract, so methods have been developed to test for fecal coliforms, which by definition are supposed to be coliform microbes that grow when a temperature of 45°C (i.e., the temperature of the human gut) is maintained during incubation. The E. coli and Enterococci, tested in this study, are fecal coliforms.

Escherichia coli – E. coli are a natural inhabitant of the human digestive tract, and are found in the large intestine. E. coli are facultatively anaerobic bacteria, which means they do not need oxygen for growth, but do better in its presence. E. coli is the most abundant microbe in the fecal coliform group but represents only 0.1% of the total microbe population in the human gut (Prescott et al. 1996).

Often, undercooked ground beef or unprocessed milk is responsible for disease due to coliforms (Prescott et al. 1996). Potential sources of E. coli in a home composting environment include meat scraps as well as natural sources. An examination of soils found evidence of total coliform, fecal coliforms, total strep and fecal strep in pasture and forest soils (Faust, 1982). The fecal coliforms in the forest soils were identified primarily as E. coli.

Enterococci spp. - These organisms are found in the small intestine of most mammals, including humans. E. faecalis is the most common member of the Enterococci group, and can cause urinary tract infections, as well as endocarditis, an infection of the heart lining, in rare cases (WebMD 2003b). Enterococci are commonly found in the gastrointestinal tract of humans and animals, and may enter the small-scale compost pile through natural sources such as animal scat.

Enterococci and fecal Streptococci are closely related, and form a subgroup of fecal coliforms (Prescott et al. 1996).

Fecal Streptococcus - Streptococci and Enterococci are closely related and part of a sizable, complicated genus of bacteria. Streptococci are non-motile and do not form endospores (i.e., Cornell Waste Management Institute Cold Compost Project – Final Report thick-walled spores that can resist heat and chemicals). Members of this group are responsible for streptococcal sore throats and rheumatic fever, but some species comprise part of the natural flora of human mouth and respiratory tract. Small-scale composts may become inoculated through post consumer food waste. For this study, fecal Streptococci were used as an indicator organism (Prescott et al. 1996).

Pathogenic Organisms Salmonella spp.-Some types of Salmonella bacteria can cause food poisoning. Salmonella are included because they may be found in a variety of foods that are added to home composts such as dairy and meat products, poultry, eggs, and fish. Salmonella survive independently of a human host, and can be transported in the intestinal tract of animals that include dogs and cats, livestock including cattle, horses, swine, sheep, and fowl, and wildlife including rodents, birds, turtles, and reptiles (Prescott et al. 1996). Home composts can be exposed to any of these, either directly or indirectly.

Infections with Salmonella can cause food poisoning, and is termed Salmonellosis. Symptoms may include diarrhea and mild fever. Less frequently muscle aches, headaches and nausea might occur. These symptoms appear because Salmonella microbes secrete enterotoxins (i.e., toxins that affect cells in the intestinal lining) and cytotoxins (i.e., toxins or antibodies that impact only certain specific cell types). The two most common species causing Salmonellosis are S.

typhimurium and S. enteritidis (Prescott et al. 1996).

Clostridium perfringens – C. perfringens is commonly found growing in reheated meat dishes, and if large quantities of this microbe are ingested, severe diarrhea can quickly occur, as well as occasional vomiting. Recovery takes place in a healthy person within 4 days, but the symptoms of C. perfringens infection can be serious. C. perfringens is naturally present in the soil and may become incorporated into composts through soil mixing. C. perfringens is also associated with food poising in cases were meat is rewarmed. Small-scale compost piles may be inoculated through natural sources or meat scraps in post consumer food waste (Prescott et al. 1996).





Cornell Waste Management InstituteCold Compost Project – Final Report

Microbial Testing The two laboratories used different methodologies for measuring bacteria. Laboratory #1 specializes in testing water samples for microorganisms, but has limited experience working with compost and solid mediums. Laboratory #2 specializes in compost testing, and has many years of experience working with and testing solid media. See Table 1 for a comparison of test methods.

Cornell Waste Management Institute Cold Compost Project – Final Report

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Cornell Waste Management Institute Cold Compost Project – Final Report As a hedge against this uncertainty, in addition to using the EPA’s “most probable number” (MPN) technique (EPA 40 CFR Part 503), we also examined compost samples by a different cultural method specific for E. coli 0157:H7 by plating on sorbitol-MacConkey-MUG agar. Low levels of fecal coliform (1000 MPN per gram dry solids) and very low Salmonella (3 MPN per 4 g solids) with a negative for E.coli 0157:H7 (at a detection limit of 1 cell/25 g solids) will be interpreted as a sign of a very hygienic compost.

Compost samples collected during early sampling were mostly analyzed by Lab 1. Lab 2 did some limited testing of non-microbial parameters on samples submitted toward the end of the early sampling. In the second round of sampling, lab 1 measured non-microbial parameters as well as C. perfringens, E. coli, Enterococci, Salmonella, total coliforms, fecal coliforms, and fecal Streptococci. Lab 2 tested for C. perfringens, E. coli, Enterococci, Salmonella, total coliforms, and fecal coliforms. Laboratory 1 changed reporting units for all of the microbes except clostridium and fecal coliform midway through the project. For example, samples collected in early 2001 reported E. coli in MPN (most probable number)/100mL but then switched in 2002 to MPN/g.

Statistical Analysis We used statistical methods to address several questions.

1. Was there a significant difference between the results from laboratory 1 and laboratory 2?

2. Could values for various compost parameters (such as pH) be correlated with microbial concentrations?

3. Was there a correlation between presence and concentration of the various microbes?

Statistical methods included ANOVA, which was used to address question 1. The variance between sample means from laboratory 1 and laboratory 2 was analyzed and then compared to the variance within each laboratory data set.

Multiple regression analysis was used to address question 2. We examined the influence of a number of independent variables on the concentration of each particular microbe (the dependent

variable). An example is provided below:

Cornell Waste Management Institute Cold Compost Project – Final Report Y = a + b1*X1 + b2*X2…bP*XP Where Y = dependent variable a = constant b1 = slope of independent variable 1 b2 = slope of independent variable 2 bp = slope of independent variable P X1 = value of independent variable 1 X2 = value of independent variable 2 Xp = value of independent variable P The independent variables used for regression analysis in this study are organic matter (OM), C:N ratio (CN), density, Total Kjeldahl Nitrogen (TKN), moisture, pH, and conductivity (salts).

The dependent variable is one of the following: Clostridium, E. coli, enterococci, fecal coliforms, fecal strep, and total coliform. Multiple regression analyses were performed to determine whether microbial concentrations could be predicted from the other variables. A test of significance was used so that results are reported only when the prediction equation was 90% more likely than “guessing” to determine the average value of whatever microbe being evaluated are reported.

Question 3 was addressed by constructing scatterplot graphs comparing one microbe to another.

For example, data for E. coli would be placed on the X-axis of a scatterplot graph, and Salmonella data would be placed on the Y-axis. The resulting r2 value, a measure of correlation strength, would then be examined to see if a relationship between the two exists. If a strong correlation is found, the curve generated by the scatterplot could be used to predict the concentration of one microbe based on the other (Figure 2).

Cornell Waste Management Institute Cold Compost Project – Final Report Figure 2. Example of a correlation graph between C. perfringens and E. coli. The slope of the line indicates the strength of relationship between factors. In this case, the line is flat and the “r2” value is close to zero, which means there is no relationship.

Results Physical Parameters The 19 compost piles included in this project represent a variety of management practices. Of these, 15 included pre-consumer food waste and 14 added post-consumer food waste, but only 2 added meat scrap. Five sites turned the compost piles.

Physical attributes of the composts varied widely among the piles as seen in Table 2. Low organic matter is typically associated with piles into which soil is mixed. The test results for physical parameters are available in Appendix C.

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Cornell Waste Management Institute Cold Compost Project – Final Report Bacterial Concentrations The following tables provide reported ranges from all of the samples for each bacterium measured from laboratory 1 and laboratory 2. Clearly there is a very wide range in what was detected. It is noteworthy that even replicate composite samples from the same site taken on the same day and analyzed by the same laboratory often exhibited more than an order of magnitude difference. Because compost is a heterogeneous material and because only small subsamples are used for bacterial testing, there is the potential for two “identical” samples to contain different pathogens and different concentrations of those pathogens. More than 4 orders of magnitude (10,000 fold) difference in several replicates was observed in a few cases.

Appendix D includes all of the test results for the bacterial analyses. The two laboratories used in this study applied different methods to measure the same set of bacteria as discussed elsewhere in this report.

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Cornell Waste Management Institute Cold Compost Project – Final Report Comparison of Pathogen Concentrations Reported by Laboratory 1 and Laboratory 2 The results from laboratory 1 and laboratory 2 for a given microbe were often different by an order of magnitude or more. Considering that both labs received subsamples taken from the same composite sample of each pile, such large differences were unexpected, although other CWMI studies have shown that compost parameters can be highly variable even at a single site, or compost pile (CWMI 2003). Using analysis of variance (ANOVA) techniques, we analyzed the data to test for a pattern of difference between the two labs.

Data for E. coli, total coliform, Enterococci, and fecal coliform were transformed to a log scale for the following ANOVA tests. Salmonella results were used “as is.” Salmonella was not log transformed because numbers detected in analysis were very low, unlike the other microbes that were often reported in the hundreds of thousands or millions. For detailed test results, see Appendix E.

C Perfringens - Results for C. perfringens from each of the two labs used were reported in different units. Laboratory 1 provided results in CFU/100 mL and Laboratory 2 gave results as MPN/g dry weight. Because of this discrepancy, and also because different methods were used to measure C. perfringens at each laboratory, only data from laboratory 2 was considered and an ANOVA was not performed.

E coli - Laboratory 1 provided results using two different units. In the earlier round of sampling, E. coli were reported as MPN/100 mL. In later sampling, results for E. coli are given in MPN/g dry weight. Thus the laboratory results could be compared for the later sampling. Laboratory 2 reported E. coli in MPN/g dry weight for all reports.

ANOVA found that Laboratory 1 reported significantly higher E. coli counts than Laboratory 2 at a 95% confidence level. Laboratory 1 averaged log 3.284 (or 1923 MPN/g) and Laboratory 2 averaged log 2.886 (769 MPN/g).

Cornell Waste Management Institute Cold Compost Project – Final Report Total Coliform – Both laboratories reported total coliforms as MPN/g dry weight in the later round of sampling, so these data were used to perform an ANOVA.

ANOVA found that lab results for total coliform were not significantly different between the labs at a confidence level of 95%.



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