Decay of Coliphages in Sewage-Contaminated. Freshwater: Uncertainty and Seasonal Effects

Transcription

1 Supporting information: Decay of Coliphages in Sewage-Contaminated Freshwater: Uncertainty and Seasonal Effects Jianyong Wu 1 *, Yiping Cao 2, Brianna Young 1, Yvonne Yuen 1, Sharon Jiang 1, Daira Melendez 1, John F. Griffith 2, Jill R. Stewart 1 1. Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, NC, 27599, United States. 2. Southern California Coastal Water Research Project Authority, Costa Mesa, CA 92626, United States. * Corresponding author: jianyong.wu@alumni.unc.edu; Tel.: , Fax: (J Wu). 14 pages 2 tables 5 figures S1

2 Figure S1. The sampling scheme for coliphage decay experiments. The samples under sunlight and shaded treatment collected at day 0 were same because the treatments were not started yet. The diagrams for sampling processes from day 2 to day 9 were skipped because they were same as those at day 0 and day 10. S2

3 Summer Winter Temperature(C) Turbidity(NTU) Conductivity (ms/cm) Salinity (ppt) Chlorophyll a(ug/l) Day DO(mg/L) Day Figure S2. Water quality of the study area during the sampling periods S3

4 Figure S3. Weather conditions in the study area during the sampling periods S4

5 Summer Winter Temperature (C) Relative humidity (%) Precipitation (inch) Visibility (mi) Hour 100 Summer Winter Hour Summer Winter Hour Summer Winter Hour Figure S4. Hourly weather conditions in the study area during the whole experimental periods. S5

6 Figure S5. Sensitivity of the decay rate of somatic coliphages to the model prior parameters, sigma (σ), a and b. Here, σ (the standard deviation) is the parameter of the error term,, following a normal distribution, and a (the minimum value) and b (the maximum value) are the parameters of the decay rate, k, following a uniform distribution. S6

7 OpenBugs code model { for(i in 1:N) { # N is the number of day, from day 0 to day 10 y1[i] ~ dpois(lambda1[i]) # y1 and y2 are coliphage counts in two sample y2[i] ~ dpois(lambda2[i]) # y1 and y2 are duplicate samples lambda1[i] <- c1[i] * v1[i] lambda2[i]<- c2[i] * v2[i] # c1 and c2 are concentrations of two samples # v1 and v2 are the volumes of two samples, unit is ml mu1[i] <- logc0 - k*t[i] #link function, lnc t =ln C 0 -kt mu2[i] <- logc0 - k*t[i] # logc0 is the coliphage concentration in day 0, # logc0 is a constant, the unit is pfu/ml. c1[i] <- exp(logc1[i]) c2[i] <- exp(logc2[i]) logc1[i] ~ dnorm (mu1[i], tau) logc2[i] ~ dnorm (mu2[i], tau) } Data } k ~ dunif(0, 10) tau <- pow(sigma, -2) sigma ~ dunif (0,20) # The decay of F+ coliphages with shade treatment list(n=1.10e+01, t=c(0.00e+00, 1.00E+00, 2.00E+00, 3.00E+00, 4.00E+00, 5.00E+00, 6.00E+00, 7.00E+00, 8.00E+00, 9.00E+00, 1.00E+01), y1=c(9.43e+02, 2.51E+03, 2.26E+02, 6.70E+01, 3.60E+01, 4.00E+00, 2.20E+01, 8.00E+00, 1.00E+00, 0.00E+00, 0.00E+00), y2=c(9.43e+02, 8.80E+01, 1.36E+02, 7.10E+01, 3.60E+01, 2.00E+01, 0.00E+00, 2.00E+00, 0.00E+00, 0.00E+00, 0.00E+00), v1=c(1.00e+02, 7.00E+01, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02), v2=c(1.00e+02, 5.00E+01, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02, 1.00E+02), logc0=2.25) S7

8 Analysis of seasonal effects on coliphage decay A variance decomposition method was used to determine influential factors for coliphage decay and apportion the seasonal effects. 1, 2 For a model y= f(x1, x2, xn), the total variance of the dependent variable y can be decomposed into the partial variance attributable to each factor (x1, x2,,xn) and the interactions of these factors. The factor that contributes to a larger partial variance has a larger influence on the dependent variable. In this study, the decay rates of coliphages were examined under three conditions: summer sunlight treatment, summer shaded treatment and winter. The decay rate in each condition was estimated with 1000 values using Bayesian modeling. Therefore, there are 3 groups of decay rates obtained for each type of coliphages. To determine the contributions of influential factors to the season effects, first, we apportioned the seasonal effect to the effect attributable to solar radiation and the remaining seasonal effect attributable the seasonal factor. Here, the seasonal effect was the combined effects due to the changes in meteorological factors and water quality variables between summer and winter, such as solar radiation, temperature, precipitation, turbidity and others. The remaining seasonal effect was the seasonal effect excluding the effect attributable to solar radiation because it was taken as a standalone factor. The seasonal factor was the combination of meteorological factors and water quality variables excluding solar radiation. In the second step, we analyzed which specific factors of the seasonal factor played an important role in the remaining seasonal effect. For F+ coliphages, the decay rates were not significantly different under sunlight and shaded treatments. Therefore, solar radiation did not have significant effects on the decay of F+ coliphage, thus, the seasonal factor contributing to the remaining seasonal effect played key roles in the variation of the decay rates between summer and winter. S8

9 In terms of somatic coliphages, the decay rates were different under three conditions, therefore, both solar radiation and the seasonal factor influenced the decay. When the decay rates of somatic coliphage under sunlight and shaded treatments were compared, the between-group variance of the decay rates was only contributed by solar radiation but not the seasonal factor, because the decay experiments were conducted in the same season (Equation 1). When the decay rates of somatic coliphages under sunlight or under shaded treatments were compared with that in winter, the between-group variance could be decomposed into the variance attributable to solar radiation, the seasonal factor and their interaction, respectively (Equation 2 and 3). V(Y 1 )= V( I 1 ) (1) V(Y 2 )= V( I 2 ) + V( season) + V( I 2, season) (2) V(Y 3 )= V( I 3 ) + V( season) + V( I 3, season) (3) Where, V(Y 1 ) is the variance of decay rates between sunlight treatment and shaded treatment in summer, I 1 is the difference of the light intensity between sunlight treatment and shaded treatment, V( I 1 ) is the variance attributable to solar radiation ( I 1 ). V(Y 2 ) is the variance of decay rates between summer (sunlight treatment) and winter, I 2 is the difference of the light intensity between summer (sun treatment) and winter, season is the variation of the seasonal factor between summer and winter. V( I 2 ), V( season) and V( I 2, season) are the partial variances attributable to solar radiation ( I 2 ), the seasonal factor and their interaction, respectively. Similarly, V(Y 3 ) is the variance of decay rates between summer (shaded treatment) and winter, I 3 is the difference of the light intensity between summer (shaded treatment) and winter, V( I 3 ), V( season) and V( I 3, season) are the partial variances attributable to solar radiation ( I 3 ), the seasonal factor and their interaction, respectively. S9

10 To solve these three equations, first, we calculated the between-group variances of the decay rates for three comparisons (sunlight vs. shaded, sunlight vs. winter, and shaded vs. winter). V(Y 1 ), V(Y 2 ) and V(Y 2 ) were calculated based on the estimated values of decay rates in three conditions. Second, we assumed that the decay rate and light intensity had a linear relationship. 3 Specifically, the increase of light intensity by n times would increase the decay rate by n times and the variance by n 2 times. Therefore, V( I 1 ), V( I 2 ) and V( I 3 ) were related to I 1, I2 and I3, respectively. The light intensity was obtained by the measurement of UVB ( nm) based on the method described in literature. 3 I 1, I2 and I3 were calculated by the measured values. According to Equation 1, V( I 1 ) can be calculated. Then, V( I 2 ) and V( I 3 ) were calculated based on the linear relationship between the decay rate and light intensity. Similarly, the variances attributable to the interaction between solar radiation and the seasonal factor in Equation 2 and 3 were also related to I2 and I3, respectively, because the remaining season effect were same in both comparisons (sunlight vs winter and shaded vs. winter). season was unknown but did not affect to solve the equation. By comparison of Equation 2 and Equation 3, V( season) were offset, while V( I2, season) and V( I 3, season) were calculated. Finally, the partial variances attributable to solar radiation, the seasonal factor and their interactions in three comparisons were calculated (Table S1). After apportioned the seasonal effect to the effect attributable to solar radiation and the seasonal factor, we analyzed which specific factors of the seasonal factor were influential on the decay rates of coliphages. In this study, we obtained the data for 7 water quality variables, including water temperature, conductivity, dissolved oxygen (DO), non-purgeable organic carbon (NPOC), turbidity, chlorophyll a (chla) and salinity, and 4 meteorological variables (air temperature, visibility, relative humidity and precipitation). Based on literature, 4 conductivity and visibility S10

11 were not influential on coliphage decay. Salinity and water ph on the decay of coliphages were not considered because salinity was very low in the fresh water samples, and water ph had little variation in the study area. DO was also not considered because a previous study found that the inactivation of F-DNA phage was independent of DO. 5 For the 7 remaining factors, we conducted one-way ANOVA to examine whether they were significantly different between summer and winter. According to the results (Table S2), relative humidity and precipitation did not show significantly seasonal variation (p>0.05), suggesting that these two factors might not significantly contributed to the remaining seasonal effect. Since air temperature and water temperature were highly correlated based on Pearson correlation analysis (r=0.94, p<0.001), these two variables could be looked as one variable, temperature. As a result, 4 factors, temperature, NPOC, chla and turbidity were major factors that might contribute to the remaining seasonal effect. Due to the limitation of the experimental design, the specific contributions from these four individual factors could not be quantified. However, the results of ANOVA showed that the seasonal variation of turbidity and NPOC was smaller than that of chla and temperature. Turbidity might affect the decay of microorganisms through two ways: 1) by reducing the UV light transmittance in water; 2) by shielding microorganisms from exposure to UV light. 6 A study showed the inactivation of coliphage MS2 caused by UV light was similar in water with different turbidities (from 0.4 NTU to 12 NTU). 7 Therefore, turbidity was expected to have a smaller contribution to the remaining seasonal effect than temperature and chla. NPOC measures organic matters in water, which can both attenuate light and produce reactive oxygen species. Studies have shown that organic matters affected the inactivation of coliphages in either way. 8, 9 Therefore. NPOC might decease or increase the decay rates of coliphages and its total effects may be offset. It could be speculated that the effect of NPOC will be smaller than that of S11

12 temperature and chla and might be also smaller than turbidity. Predation by algae or other microorganisms is another mechanism of virus decay. Since chla concentration is often used as an indicator of algae biomass, a higher concentration of chla might be associated with a higher decay rate of coliphages. 10 Therefore, chla is also an important factor for coliphage decay. However, because chla concentration is dependent of temperature (the Pearson correlation analysis showed both variables were significantly correlated), 11 its effect on coliphage decay could be looked as an indirect effect of temperature on coliphage decay. Besides affecting chla, temperature has direct effects on colipahge decay (e.g., affect the attachment and multiplication of viruses). 4 Therefore, temperature could have a larger effect on coliphage decay than chla. Based on the above analysis, the order of the contribution to the remaining seasonal effect by 4 factors might be: temperature > chla > turbidity > NPOC. Literature cited 1. Saltelli, A.; Tarantola, S.; Chan, K.-S., A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 1999, 41, (1), Wu, J. Y.; Simmons, O. D.; Sobsey, M. D., Uncertainty analysis of the recovery of hollow-fiber ultrafiltration for multiple microbe classes from water: A Bayesian approach. J. Microbiol. Methods 2013, 93, (3), Maraccini, P. A.; Mattioli, M. C. M.; Sassoubre, L. M.; Cao, Y. P.; Griffith, J. F.; Ervin, J. S.; Van De Werfhorst, L. C.; Boehm, A. B., Solar Inactivation of Enterococci and Escherichia coli in Natural Waters: Effects of Water Absorbance and Depth. Environ. Sci. Technol. 2016, 50, (10), U.S.EPA, Review of coliphages as possible indicators of fecal contamination for ambient water quality. In 820-R : Washington, D.C, Davies-Colley, R. J.; Donnison, A. M.; Speed, D. J.; Ross, C. M.; Nagels, J. W., Inactivation of faecal indicator microorganisms in waste stabilisation ponds: Interactions of environmental factors with sunlight. Water Res. 1999, 33, (5), S12

13 6. Passantino, L., Effect of low turbidity and algae on UV disinfection performance. J Am Water Works Ass 2004, 96, (6), Liu, W. J.; Zhang, Y. J., Effects of UV intensity and water turbidity on microbial indicator. J. Environ. Sci. 2006, 18, (4), Chung, H.; Sobsey, M. D., Comparative Survival of Indicator Viruses and Enteric Viruses in Seawater and Sediment. Water Sci Technol 1993, 27, (3-4), Silverman, A. I.; Peterson, B. M.; Boehm, A. B.; McNeill, K.; Nelson, K. L., Sunlight Inactivation of Human Viruses and Bacteriophages in Coastal Waters Containing Natural Photosensitizers. Environ Sci Technol 2013, 47, (4), Suttle, C. A.; Feng, C., Mechanisms and Rates of Decay of Marine Viruses in Seawater. Appl Environ Microb 1992, 58, (11), Murata, N.; Fork, D. C., Temperature dependence of chlorophyll a fluorescence in relation to the physical phase of membrane lipids algae and higher plants. Plant physiology 1975, 56, (6), S13

14 Table S1. The variance decomposition of the decay rates of somatic coliphages under different scenarios Between-group Variance decomposition Comparison of variance of decay decay rates Solar radiation The seasonal rates Interaction (UVB) factor Summer (Sun) vs. Summer (Shaded) (100%) 0 (0%) 0 (0%) Summer (Sun) vs. Winter (12.1%) (38.2%) (49.7%) Summer (Shaded) vs. Winter (0.9%) (2.9%) (96.2%) Table S2. The seasonal variation of water quality and meteorological variables between summer and winter examined with one-way ANOVA Variables Sample size Mean ± SD (Summer) Mean± SD (Winter) F Value Water temperature (ºC) ± ± <0.001 Turbidity (NTU) ± ± Chlorophyll a (µg/l) ± ± <0.001 NPOC (mg/l)* ± ± DO (mg/l) ± ± <0.001 Air temperature (ºC) ± ± <0.001 Relative humidity (%) 11 66±8 62± Precipitation (inch) 11 0±0 0.01± NPOC: non-purgable organic carbon. The data for NPOC are from the literature. 3 p S14