Modeling the Endogenous Sunlight Inactivation ... - ACS Publications

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Modeling the Endogenous Sunlight Inactivation Rates of Laboratory Strain and Wastewater E. coli and Enterococci Using Biological Weighting Functions Andrea I. Silverman*,†,‡,§ and Kara L. Nelson†,‡ †

Engineering Research Center for Re-Inventing the Nation’s Urban Water Infrastructure (ReNUWIt), Berkeley, California 94720-1710, United States ‡ Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720-1710, United States S Supporting Information *

ABSTRACT: Models that predict sunlight inactivation rates of bacteria are valuable tools for predicting the fate of pathogens in recreational waters and designing natural wastewater treatment systems to meet disinfection goals. We developed biological weighting function (BWF)-based numerical models to estimate the endogenous sunlight inactivation rates of E. coli and enterococci. BWF-based models allow the prediction of inactivation rates under a range of environmental conditions that shift the magnitude or spectral distribution of sunlight irradiance (e.g., different times, latitudes, water absorbances, depth). Separate models were developed for laboratory strain bacteria cultured in the laboratory and indigenous organisms concentrated directly from wastewater. Wastewater bacteria were found to be 5−7 times less susceptible to full-spectrum simulated sunlight than the laboratory bacteria, highlighting the importance of conducting experiments with bacteria sourced directly from wastewater. The inactivation rate models fit experimental data well and were successful in predicting the inactivation rates of wastewater E. coli and enterococci measured in clear marine water by researchers from a different laboratory. Additional research is recommended to develop strategies to account for the effects of elevated water pH on predicted inactivation rates.

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cell (e.g., nucleic acids,18,19 flavins,20 porphyrins,18 NAD/ NADH),21 and each chromophore has a distinct light absorption spectrum. In general, sensitivity of bacteria and viruses to endogenous inactivation tends to decrease with increasing wavelength in the sunlight range [e.g., UVB (280− 320 nm) > UVA (320−400 nm) > visible light (400−700 nm)].22−29 Approaches that account for the wavelength dependence of sunlight inactivation rates have been used to model virus inactivation9,13,14,16,17 but have been limited for modeling bacteria inactivation rates.30,31 Early bacteria sunlight disinfection models were created as empirical equations that described first-order fecal coliform disinfection rates in waste stabilization pond systems. The pond disinfection model developed by Marais in 1976,32 for example, was based on the Arrhenius equation, used temperature as the sole input, and therefore did not account for critical sunlight disinfection parameters like irradiance, light absorbance by the water, and

INTRODUCTION Sunlight exposure is a dominant mode of disinfection of bacteria and viruses in sunlit waters and is often found to be more important for bacteria and virus removal than sedimentation or other dark processes.1 Consequently, sunlight disinfection has important implications for recreational water quality in sewage-impacted waters2 and disinfection in natural wastewater treatment systems utilizing open-water ponds1 or wetlands.3 Given the importance of this natural disinfection process, many researchers have focused on understanding sunlight inactivation mechanisms with a goal of developing mathematical models to predict sunlight inactivation rates.4−17 The rationale behind this research is that improved inactivation rate models can be used to predict the fate of pathogens in recreational waters and design natural wastewater treatment systems to meet disinfection targets. Bacteria and virus susceptibilities to the three sunlight disinfection mechanismsthe direct and indirect endogenous mechanisms and the exogenous mechanismhave wavelength dependencies. For example, the two endogenous inactivation mechanisms, which cannot be separated experimentally and are thus considered jointly as “endogenous inactivation”, are initiated by photon absorption by chromophores within the © XXXX American Chemical Society

Received: July 25, 2016 Revised: October 5, 2016 Accepted: October 25, 2016

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DOI: 10.1021/acs.est.6b03721 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology

19433, which were stored as glycerol stocks at −80 °C. Batch cultures of laboratory strain bacteria were prepared fresh for each experiment using a protocol outlined in detail in the Supporting Information. Bacteria were grown to stationary phase and washed before inoculation into modified PBS (20 mM NaH2PO4, 10 mM NaCl, pH 7.5). Indigenous bacteria were concentrated from wastewater and washed to create stock solutions for inactivation experiments using a method developed by Nguyen et al.15 (details are provided in the SI). A new stock solution was produced for each experiment. Nitrified, clarified, nondisinfected wastewater from the effluent of an oxidation ditch in Discovery Bay, CA,15,16,36,37 was pumped through presterilized, positively charged cartridge filters (NanoCeram VS2.5-5; Argonide Corp.).38 For each stock solution, two cartridge filters were used to filter a total volume of wastewater that ranged between 250 and 370 L. To elute, 450 mL of beef extract (3% beef extract, 3% Tween 80, 0.3 M NaCl, pH 9) was added to the filter housing and held for 15 min; then positive pressure (N2 gas) was used to pass an additional 550 mL of beef extract through the filter and into a sterile beaker. Bacteria collected in the eluate were washed and concentrated through the following steps: the eluate was centrifuged at 9000 × g for 10 min; pellets were resuspended in PBS (5 mM NaH2PO4, 18 mM Na2HPO4, 145 mM NaCl, pH 7.5), which was then centrifuged at 500 × g for 5 min to remove particles; the supernatant was collected and centrifuged at 8000 × g for 5 min, and the pellets were resuspended in modified PBS to a final volume of 150 mL. Wastewater bacteria solutions were stirred magnetically at room temperature overnight before use in experiments. Overnight stirring was performed because during preliminary experiments with a shorter mixing time (∼30 min) bacteria concentrations increased during the first few hours of the experiment (data not shown), which was attributed to disaggregation of bacteria aggregates. When wastewater bacteria solutions were mixed overnight, no increase in bacteria concentrations was observed over the course of the experiment. Cutoff Filter Experimental Design. Experimental methods were similar to those used in our laboratory previously.8,9,15,16 Separate experiments were conducted with laboratory-cultured and wastewater-sourced bacteria. Bacterial solutions were aliquoted into experimental reactors with total liquid volumes of 50 (laboratory E. coli and E. faecalis, which were cospiked) or 15 mL (wastewater bacteria). Reactors consisted of 5 cm diameter glass beakers that were painted black. Initial bacteria concentrations were approximately 107 and 104 colony-forming units (CFU)/mL for each species of laboratory-cultured and wastewater-sourced bacteria, respectively. Different bacteria starting concentrations could affect average light transmission through the water column; therefore, irradiance was corrected for light attenuation for each of the bacteria solutions (described below). Five-cm square, glass, long-pass cutoff filters were placed on top of each reactor to modify the incident irradiance spectrum. The filters used were Schott WG305 (305 nm), Schott WG320 (320 nm), Schott KG5 (335 nm), Kopp 9345 (345 nm), Hoya L39 (390 nm), Schott GG455 (455 nm), and Schott OG515 (515 nm); 50% transmittance wavelengths are provided in parentheses (Figure S1). One reactor was left uncovered for exposure to fullspectrum simulated sunlight, and one was covered with aluminum foil as a dark control. Reactors were stirred magnetically and maintained at 20 °C in a water bath with a recirculating chiller. The pH of the experimental solutions was

depth. In their 1987 paper, Sarikaya and Saatci33 presented an improved pond disinfection model that included factors affecting sunlight exposure. However, this model did not allow for wavelength-specific values of irradiance or light absorbance. More recently, Nguyen et al. 15 modeled endogenous E. coli and enterococci inactivation rates based on the sum of irradiance in the UVA and UVB regions (λ = 280−400 nm) given that light across this wavelength range is principally responsible for the endogenous inactivation of bacteria.7 However, predicting inactivation rates based on the irradiance of light within a band of wavelengths may not work at depth in the water column or across all sunlight spectra if the spectral distribution of the light changes (e.g., with changes in season, time of day, latitude, or atmospheric conditions).13,16 A second shortcoming of some existing bacterial sunlight inactivation rate models, including those employed by Mostafa et al.30 and Lui et al.,31 is that they were created using data from experiments conducted with laboratory-cultured bacteria. Previous studies that directly compared disinfection of laboratory-cultured and wastewater-sourced bacteria found indigenous wastewater E. coli and enterococci to have slower sunlight inactivation rates than their laboratory-cultured counterparts.15,34 Additionally, experimental bacteria sourced directly from wastewater had slower inactivation rates than those isolated from wastewater and grown up in the laboratory before use in experiments.15,35 These findings are important for the development of sunlight inactivation rate models, given that equations derived from experimental data utilizing laboratorycultured bacteria may overestimate the inactivation rates of bacteria in wastewater, which are the actual targets of disinfection. To predict the total sunlight inactivation rates of waterborne microorganisms, our overall strategy involves modeling endogenous and exogenous inactivation rates separately as a way to account for different susceptibilities of microorganisms to each mechanism, the differing wavelength-dependencies of the two mechanisms, and the multiple ways chromophores in the water column can affect inactivation rates (i.e., reducing endogenous inactivation rates due to light screening while increasing exogenous inactivation rates by acting as photosensitizers).8,15,16 The goal of this study was to improve our ability to predict overall inactivation rates by focusing on the development of numerical models for endogenous sunlight inactivation rates of the bacteria E. coli and enterococci using biological weighting functions (BWFs). BWFs describe the sensitivity of an organism to each wavelength of light in the presence of a polychromatic spectrum.24,25 By accounting for wavelength-specific sensitivity of bacteria to sunlight, the BWFbased approach can predict endogenous inactivation rates under a diverse range of light and water quality conditions. To accomplish this task, inactivation experiments were conducted with both laboratory-cultured and wastewater-sourced E. coli and enterococci using simulated sunlight. Experimental data were used to create BWF-based endogenous inactivation rate models using the polychromatic approach described by Rundel25 and Cullen and Neale.24

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METHODS Bacteria. Experiments were conducted with laboratory strain bacteria that were cultured in the laboratory (“laboratory cultured”) as well as bacteria that were concentrated directly from wastewater (“wastewater sourced”). The laboratory strains were Escherichia coli NCM 4236 and Enterococci faecalis ATCC B


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Environmental Science & Technology ⎛ C i,j ⎞ i,j t ln⎜ t i ⎟ = −kobs ⎝ C0 ⎠

7.5, and the dissolved oxygen concentrations remained close to saturation (e.g., 9 mg/L). Replicate data were collected on different days; there were between three and six replicates for each reactor. Reactors were exposed to simulated sunlight using a 1000 W solar simulator (Oriel, 91194) with an ozone-free Xe bulb (Newport, 6271), 1.5:G:A airmass filter (Newport, 81388), and atmospheric attenuation filter (Newport, 81017). Reactors were exposed to simulated sunlight for up to 48 h. Subsamples were collected intermittently for enumeration of E. coli and enterococci using the spread plate method with 20 μL inocula; samples were serially diluted in PBS when necessary. Selective media mTEC (BD, 214884) and mEnterococcus (BD, 274620) were used for enumeration of E. coli and enterococci, respectively, to exclude growth of other bacteria present in wastewater.15 Bacteria were quantified as CFU; the limit of detection was 50 CFU/mL. An additional experiment was conducted to determine whether the indigenous bacteria extraction protocol had an effect on inactivation rates. During this experiment, a whole wastewater sample (i.e., wastewater that was not subjected to any processing steps) was exposed to full-spectrum simulated sunlightsubsamples were collected intermittently for enumeration of indigenous bacteria using the methodology described above. The wastewater used for this experiment was collected from the same location as the wastewater used to extract indigenous bacteria; the average pH was 8.6. The irradiance spectrum emitted by the solar simulator [Ed(0,λ); W m−2 nm−1] was measured before each experiment using a spectroradiometer (Stellarnet, EPP2000C-SR-100, CR2 cosine receptor). Filter and experimental solution absorbance spectra were measured using a UV−vis spectrophotometer (Shimadzu UV-2600; l = 1 cm; Figure S1). The depth-averaged irradiance spectrum transmitted through the water column in each reactor j [⟨Ej0(z,λ)⟩; W m−2 nm−1] was calculated by correcting Ed(0,λ) for cutoff filter transmittance and light absorbance within the water column. Detailed methodology for calculating ⟨Ej0(z,λ)⟩ is provided in the SI, and ⟨Ej0(z,λ)⟩ for each reactor are presented in Figure S1. Inactivation Kinetics. Inactivation rates were calculated using two kinetic models. The first was a multitarget inactivation model that accounts for the shoulder observed in some sunlight inactivation curves4,5,34,39 Cti , j i,j mi i = 1 − (1 − exp( − kobst )) C0

(2)

ln(Ci,jt /Ci0) values were calculated for each time point and replicate, pooled for each reactor, and plotted versus t to calculate ki,jobs ± the standard error of the slope by linear regression, with the line forced through the origin. To compare ki,jobs determined by the two kinetic model approaches, the times i,j required for three-log bacteria inactivation (k99.9 ) were calculated for each model. While the multitarget model fit the data slightly better than the log−linear first-order decay model (i.e., had higher r2; Figures S2−5), ki,j99.9 predicted by the two approaches differed by less than 5% (Tables S1 and S2). Additionally, ki,jobs determined by the log−linear first-order decay equation is more conducive to incorporation in the overall sunlight inactivation model. Therefore, ki,jobs calculated using the log−linear first-order decay equation were used to develop the spectral sensitivity spectra described below. When comparing ki,jobs between cutoff filters or between bacterial solutions with different absorbance spectra or depths (i.e., between laboratory-cultured and wastewater-sourced bacterial solutions), ki,jobs were calculated based on photon fluence [m2(mol photon)−1; λ = 290−700 nm; see the SI for details] instead of time. Normalizing ki,jobs by photon fluence allowed us to account for the reduction in the number of photons transmitted through the water column when using higher wavelength cutoff filters or experimental solutions with a greater amount of light screening. Determining Spectral Sensitivity Coefficients. The framework used to model endogenous inactivation rates (kiendo; h−1) was based on the work of Fisher et al.,9 where Pi(λ) (m2 W−1 h−1) are defined as sensitivity coefficients that describe the sensitivity of a microorganism to each wavelength of light and Δλ is the wavelength interval between Pi(λ) values (in this case equal to 1 nm) i kendo =

∑ P i(λ)⟨E0(z , λ)⟩Δλ λ

(3)

Using this approach, the only inputs needed for predicting kiendo in a well-mixed water column of depth z are the irradiance spectrum incident on the water surface [Ed(0,λ)] and the water absorbance spectrum [αs(λ)], which are used to calculate ⟨E0(z,λ)⟩ (eq S2). To determine Pi(λ) for each bacterial population, we followed an approach for determining BWFs using polychromatic light that was outlined by Rundel25,40 and Cullen and Neale.24 Given that we used polychromatic light to determine inactivation rates, Pi(λ) spectra are defined to be BWFs, as opposed to photoaction spectra, which are typically determined through exposure to monochromatic light.24,25 In modeling sunlight inactivation rates of bacteria, BWFs are preferred over photoaction spectra, given that sunlight is a polychromatic light source, and inactivation experiments conducted using monochromatic light sources may not account for complex biological responses to a broad band of wavelengths.24 As suggested by Rundel25 and Cullen and Neale,24 we assumed a spectral form for Pi(λ) defined by the following exponential equation

(1)

where Ci0 is the initial bacteria concentration (CFU mL−1) and was calculated as the average initial bacteria concentration among reactors from the same experiment. For each bacterium (i) and reactor (j), Ci,jt is the bacteria concentration measured at time t (h), ki,jobs (h−1) is the first-order inactivation rate constant, and mi is a shoulder constant. Ci,jt /Ci0 values were calculated for each time point and replicate and were pooled for each reactor i,j to calculate kobs and mi by nonlinear regression. More specifically, ki,jobs and mi were determined by using Solver (Microsoft Excel) to minimize the root-mean-square error (RMSE) between predicted and observed Ci,jt /Ci0. mi was determined separately for each of the four bacterial populations we investigated but was held constant across reactors for each population. The second kinetic model used to calculate inactivation rates was a log−linear first-order decay model

i

i

i

Pi(λ) = e−(a0 + a1(λ − λr ))

(4)

where and are fitting coefficients determined for each bacterial population. Solver (Microsoft Excel) was used to solve for ai0, ai1, and λir in eq 5 through simultaneously minimizing the ai0,

C

ai1,

λir


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Figure 1. First-order observed inactivation rates (kobs) of laboratory-cultured and wastewater-concentrated E. coli (a) and enterococci (b) for each cutoff filter. (c) Comparison of measured kobs of laboratory and wastewater bacteria inoculated into PBS with kobs of the bacteria in whole wastewater, with exposure to full-spectrum simulated sunlight. To account for differences in water depth and water absorbance across experiments, all kobs are calculated based on average photon fluence transmitted through the water column (λ range is 290−700 nm). Error bars represent 95% confidence intervals; some error bars are smaller than the symbols.

RMSE between the predicted (ki,jendo) and the observed (ki,jobs) inactivation rates from all eight cutoff filter conditions 700 i,j kendo =

∑ λ = 290

i

i

mask the effect of this simplification. Tables S1 and S2 present ki,jobs and ti,j99.9 values for the first-order decay and multitarget models, respectively, for all bacteria and reactors. For all four bacteria populations evaluated, inactivation rates decreased with increased cutoff wavelength, relative to both time and photon fluence (Figures 1 and S6). Photon fluencebased ki,jobs were used to normalize inactivation rates to the average number of photons transmitted through the water column for each cutoff filter. This correction is important for comparison among cutoff filters, given that higher wavelength cutoff filters transmit fewer photons. Laboratory-cultured and wastewater-sourced E. coli inactivation rates were not significantly different than zero when wavelengths shorter than 455 nm were removed from the spectrum, meaning that longer wavelengths did not contribute to E. coli inactivation. Conversely, some inactivation of enterococci was still observed with the use of the 515 nm cutoff filter. Inactivation rates in the dark controls were not significantly different from zero for the laboratory-cultured bacteria, demonstrating that all inactivation in PBS was attributed to sunlight exposure. Some inactivation was observed for wastewater-sourced bacteria in dark controls at time points greater than 30 h. We attributed this degradation to natural, age-related attenuation: due to their passage through the sewer system and wastewater treatment plant before collection and stirring overnight before experiments, the wastewater bacteria were at least 24 h older than the laboratory strain bacteria. Therefore, only data collected at time points of 30 h and less were used to determine ki,jobs of wastewatersourced bacteria. Laboratory-Cultured versus Wastewater-Sourced Bacteria. Given that we extracted bacteria from wastewater for use in experiments, as opposed to conducting disinfection trials using bacteria in whole wastewater, we were able to compare endogenous inactivation rates among bacteria populations without confounding factors introduced by constituents in wastewater, such as exogenous chromophores that can absorb light (decreasing endogenous inactivation rates), and contribute to the production of exogenous reactive intermediates (increasing exogenous inactivation rates). Inactivation rates of the wastewater-sourced bacteria were lower than those of the laboratory-cultured bacteria for all cutoff filters (Figure 1 and Table S1). With exposure to full-spectrum simulated sunlight, for example, kobs of laboratory E. coli and enterococci were 7.0 and 5.7 times faster than their wastewatersourced counterparts, respectively. Previous studies also

i

e−(a0 + a1(λ − λr ))·⟨E0i , j(z , λ)⟩·Δλ (5)

As suggested by Cullen and Neale,24 we also tried to fit an equation for Pi(λ) that included an additional ai2(λ − λir)2 term within the exponent. This second-order term did not reduce the RMSE between ki,jendo and ki,jobs and was consequently not included. The shortest wavelength included in the calculated Pi(λ) spectra was 290 nm because there was no irradiance at wavelengths shorter than 290 nm in the simulated sunlight. The 95% confidence intervals (CI) for Pi(λ) spectra were calculated through bootstrap analysis. For each bacterial population i and cutoff filter j, a random value of ki,j was selected from within the 95% CI calculated for ki,jobs for that filter. This was repeated 1000 times, resulting in 1000 sets of 8 ki,j values. For each of these ki,j sets, ai0, ai1, and λir were determined using the method described above, which resulted in 1000 Pi(λ) spectra. For each wavelength, the 5th and 95th percentile Pi(λ) values were selected as the lower and upper i 95% CIs. The resulting CI spectra [P95%CI,upper (λ) and i P95%CI,lower(λ)] can be used to calculate the 95% CI on ki,jendo by simply replacing Pi(λ) in eq 3.

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RESULTS AND DISCUSSION Sunlight Inactivation Kinetics. Shoulders were observed in the inactivation curves of all four bacteria populations investigated (Figures S2−5); shoulders were most pronounced for laboratory-cultured E. faecalis and wastewater-sourced E. coli. While the multitarget kinetic model had a slightly better fit i,j with the observed inactivation data, we chose to use kobs determined using the log−linear first-order decay model for subsequent analyses, including calculation of spectral sensitivity coefficients. This decision was motivated by the larger goal of this work, which was to develop predictive equations for endogenous sunlight inactivation rates that can be incorporated with transport and hydraulic models to predict the fate of bacteria in sunlit waters, including open-water wastewater treatment systems and recreational waters. Incorporating a kinetic expression with a shoulder greatly complicates modeling transport. While there are limitations to this approach, there are many uncertainties and sources of error that can exist in modeling decay in complex environmental systems, which likely D


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Environmental Science & Technology Table 1. Pi(λ) Equations for Each Bacteriaa Pi(λ) equation

organism laboratory-grown E. coli laboratory-grown E. faecalis wastewater-sourced E. coli wastewater-sourced enterococci

P(λ) P(λ) P(λ) P(λ)

= = = =

exp(−(0.71 exp(−(0.76 exp(−(0.74 exp(−(0.69

+ + + +

0.045 0.051 0.051 0.062

× × × ×

(λ (λ (λ (λ

− − − −

332.1))) 332.2))) 282.2))) 290.6)))

NRMSE

slope of ki,jobs vs ki,jendo

0.082 0.101 0.073 0.118

0.98 0.96 1.00 1.05

a i

P (λ) were determined using the approach described by Rundel25 and Cullen and Neale;24 ki,jobs used as inputs to develop the equations were calculated using linear first-order decay equations. Normalized root-mean-square errors (NRMSE) were calculated by dividing the RMSE by the average of ki,jobs for that particular bacteria.

Figure 2. Relative E. coli sensitivity coefficients measured by previous researchers22,23,26,28,29,31 compared to laboratory-cultured E. coli Pi(λ) determined in the present study (represented by the solid black line). Sensitivity coefficients from each study are normalized to the coefficient at 280 nm from that study; Pi(λ) from this publication are normalized to the sensitivity coefficients determined by Beck et al.52 and Oguma et al.27

observed fecal coliform,4 E. coli,15,34 and enterococci15,34 sourced from wastewater to have greater resistance to sunlight inactivation than laboratory-cultured bacteria. A number of factors can contribute to the comparative resistance of wastewater bacteria. Growth conditions, including growth rate and access to nutrients, differ drastically for wastewater bacteria (which grow slowly in the nutrient-limited gut environment) and laboratory-cultured bacteria (which are typically grown in batch culture with nutrient-rich conditions, resulting in exponential growth with very short doubling times) and could affect inactivation rates. For example, for E. coli41 and E. faecalis42 cultured in chemostats, faster growth rates resulted in increased sensitivity to UVA light. Additionally, iron, which is readily available in many laboratory culture mediums but limited in the gut environment,43 has been attributed to an increase in sunlight inactivation rates of E. coli because it causes higher levels of intracellular iron,44 which has been proposed to contribute to the photofenton process and, therefore, enhanced sunlight inactivation rates.45 Within a bacterial species there are multiple strains that exist due to genetic differences between cells. Strain level differences between indigenous bacteria (which are composed of a mixed community of strains) and those grown in the laboratory (which tend to be closer to a monoculture) could be responsible for differences in observed inactivation rates.15,46,47 Additionally, if a mixed population is exposed to environmental stress (such as in wastewater), the surviving bacterial community is likely to have a greater percentage of more resistant individuals than the original population.4,5 This difference could also be attributed to the induction of bacterial stress responses that are initiated when cells enter the stationary

phase or are exposed to conditions such as starvation, DNA damage, and nonoptimal pH, temperature, and osmolarity (i.e., conditions that can be found in the gut or wastewater).48 The expression of stress response genes helps bacteria survive the original source of stress and can make the cell more resistant to additional stresses, including oxidative stress that comes with exposure to sunlight.48−50 For example, E. coli that lacked sigma factor RpoS, which regulates the general stress response, were found to be more susceptible to sunlight inactivation.50 To ensure that the slower inactivation rates of wastewatersourced bacteria were not due to stress induced when the cells were harvested from wastewater, inactivation rates were also measured in experiments conducted using whole wastewater samples (Figure 1c). The bacteria evaluated during the whole wastewater experiments were not extracted from or added to the wastewater sample and therefore represented indigenous bacteria that were not exposed to the harvesting protocol. Fluence-based kobs were compared to account for differences in light attenuation among solutions. While inactivation rates of E. coli and enterococci in whole wastewater were slightly greater than those of the wastewater-sourced bacteria that were inoculated into PBS, they were still much lower than the inactivation rates of laboratory strain bacteria. While we acknowledge that the comparison of inactivation rates between wastewater-sourced bacteria and those in whole wastewater is not perfectgiven that bacteria in whole wastewater could be exposed to additional disinfection processes, such as exogenous inactivationthese results do suggest that the resistance of wastewater-sourced bacteria to sunlight inactivation is not an artifact of their extraction method. E


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used the data points before and after to extrapolate a sensitivity coefficient for 280 nm. We normalized our laboratory E. coli Pi(λ) by the average Pi(280 nm) from Beck et al.52 and Oguma et al;27 we felt that normalizing our Pi(λ) based on this data was appropriate given that these studies calculated inactivation rates using a first-order decay equation, and fluence-based ki,jobs can be easily converted to Pi(λ). However, we acknowledge that the use of monochromatic light by the other two studies could misrepresent sensitivity to polychromatic light, given that exposure to a broad band of wavelengths can lead to complex biological responses (for example, bacteria repair mechanisms stimulated by UVA light).24 We were unable to conduct the same analysis for the other three bacteria populations we studied given that there are fewer data in the literature. The normalized photoaction spectra of the five studies mentioned above follow similar curves in the UVC and UVB regions; the data from each study diverge in the UVA region, although they maintain similar slopes. As discussed by previous researchers, the photoaction spectrum for E. coli inactivation tends to follow the absorbance spectrum of DNA in the UVC and UVB regions, where there is a steep decrease in sensitivity coefficients until approximately 313 nm.22,26,28,29 At wavelengths greater than 313 nm, the slope of the photoaction spectrum becomes less steep and diverges from the DNA absorbance spectrum. The shape of the photoaction spectrum curve for E. coli lethality, along with data from targeted mechanistic experiments, has allowed researchers to suggest that UVC and UVB light inactivates bacteria mainly through mechanisms involving photon absorption by DNA (e.g., formation of pyrimidine dimers),18,22 whereas inactivation by light in the UVA and visible range involves photon absorption by chromophores in the cell other than DNA (e.g., quinones, porphyrins, flavins, NAD),21 leading to photosensitized damage to the membrane,29 proteins,53,54 or DNA.22 The normalized Pi(λ) spectrum for laboratory-cultured E. coli determined in the present study was in the same range as the normalized photoaction spectra determined by the previous studies for UVA light, and all curves had a similar slope. However, in the UVB region, Pi(λ) determined in the present study were much lower than the other researchers’ values. Our Pi(λ) values were likely underestimated in the UVB region due to two methodological reasons. First, our solar simulator did not emit enough light in the UVB region to capture bacteria sensitivity; this limitation could be addressed in future work by including additional experiments conducted with a light source that emits more UVB and UVC. Second, fitting Pi(λ) to a firstorder exponential model alone will not allow the spectral shape to capture the large magnitude increase in Pi(λ) that occurs with decreasing wavelength in the UVB region. This second limitation can be overcome by creating a biphasic model for Pi(λ) that is fit with a first-order exponential equation in the UVA and visible light regions [i.e., PiUVA/vis (λ) = e−(a0+a1λ)] and a second-order exponential equation in the UVB and UVC 2 i (λ) = e−(a0+a1λ+a2λ )], as suggested by Lui et regions [i.e., PUVB/C al.31 Fitting a second-order exponential equation to the UVB and UVC action spectra data from the previous studies resulted in eq 6, which is represented by the dashed red line in Figure 2, although it is important to note that eq 6 was not created from data collected in the present study and should be validated with experimental data before it is used.

Regardless of the cause of increased resistance, it is important to include wastewater-sourced microorganisms when creating inactivation rate models, given that it is these microorganisms and pathogens that are targeted for disinfection. Therefore, we created spectral sensitivity coefficients for both laboratorycultured and wastewater-sourced bacteria. This modeling approach is an alternative to that developed previously by Nguyen et al.,15 who predicted wastewater bacteria kobs by applying empirically determined factors (r) to correct kobs estimated by models developed for laboratory bacteria (r = 1.77 for E. coli and 3.06 for nonpigmented enterococci; note that r values reported by Nguyen et al. indicate smaller differences between kobs measured for lab and wastewater bacteria than observed in the present study). Spectral Sensitivity Coefficients. Pi(λ) (Figure S8) were determined based on the spectral shape suggested by Rundel25 and Cullen and Neale24 and through minimizing the RMSE between experimentally observed inactivation rates and those predicted using the Pi(λ) spectra (eq 5). The Pi(λ) equations for each type of bacteria (laboratory-cultured and wastewatersourced E. coli and enterococci) are provided in Table 1, and values for their 95% confidence intervals are provided in the Supporting Information spreadsheet. There was very good agreement between measured ki,jobs and predicted ki,jendo using the Pi(λ) spectra: the slopes of the lines of ki,jobs versus ki,jendo were between 0.96 and 1.05 (Table 1, Figure S8; slope = 1 corresponds to a perfect fit). Wastewater bacteria Pi(λ) spectra were smaller in magnitude than those for laboratory bacteria due to their slower inactivation rates. The Pi(λ) spectra presented here were developed specifically for predicting sunlight inactivation rates and include sensitivity coefficients for wavelengths between 290 and 700 nm. At wavelengths greater than approximately 450 nm, sensitivity to light, and therefore Pi(λ), was low and had little effect on overall inactivation rates of any of the bacteria we studied. Conversely, in the UVB region, previous researchers found sensitivity to light to increase by several orders of magnitude as the wavelength decreases from 300 to 280 nm, as illustrated in Figure 2.22,23,26,28,29,51 For example, Beck et al.52 and Oguma et al.27 found that exposing E. coli to light emitted by UV LEDs with a narrow bandwidth centered at 280 nm resulted in inactivation rates of 0.31 and 0.29 cm2 mJ−1, respectively; when averaged and subjected to unit conversion, these inactivation rates correspond to Pi(280 nm) equal to 242 m2 W−1 h−1, which is 2 orders of magnitude greater than laboratory-grown E. coli Pi(300 nm) determined in this study (equal to 2.1 m2 W−1 h−1). Given that the solar simulator used for experiments did not emit light below 290 nm, we were unable to extend our calculated Pi(λ) spectra to 280 nm. Additionally, given low light intensity between 290 and 300 nm, it is possible that our Pi(λ) spectra do not accurately represent sensitivity of bacteria to light at these wavelengths. During the 1970s and 1980s, several researchers developed photoaction spectra for E. coli as a means of determining the inactivation mechanisms of bacteria exposed to UV light. To compare our E. coli Pi(λ) with this previous body of work, Graph Grabber (Quintessa) was used to collect data from figures in Calkins at al.,26 Kelland et al.,29 Peak et al.,28 Tyrrell,23 and Webb and Brown22 that present photoaction spectra for inactivation of wild-type, laboratory-grown E. coli (Figure 2); tabulated data from Lui et al.31 was included as well. Each photoaction spectrum was normalized by sensitivity to 280 nm light; if no data point was available for 280 nm then we

2

E . coli PUVB/C (λ) = 242·e−(205.2 − 1.6λ + 0.003λ )

F

(6)


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conducted to develop the Pi(λ) spectra. Both waters were free of photosensitizers, had dissolved oxygen (DO) concentrations around saturation, and had circumneutral pH. Conversely, our model underpredicted E. coli kobs measured by Nguyen et al.15 by a significant amount, which we attribute to elevated DO and pH in the wetland water as compared to the PBS used in experiments to develop the Pi(λ) spectra. Elevated DO6,7 and pH6,58 can lead to greater inactivation rates of both E. coli and enterococci, with E. coli being more sensitive. While we have confidence that our model can estimate kendo of indigenous wastewater bacteria in clear waters with neutral pH and DO, such as marine recreational waters, it is likely to underestimate kendo in natural wastewater treatment systems with elevated pH and DO. Additional research is needed to determine correction factors that can be used to adjust predicted kendo for different DO and pH. An additional challenge with the use of Pi(λ) spectra to calculate endogenous inactivation rates is that measured Ed(0,λ) spectra (i.e., the main input to the kendo model) can differ depending on the individual spectroradiometer used for measurement.59−61 While part of this uncertainty is attributed to differences in spectroradiometer calibration conditions (including calibration lamp traceability, lamp position, temperature, and stability and accuracy of current to the lamp), some of the discrepancy between measurements can be attributed to differences in the underlying measurement mechanisms across spectroradiometer models (see Bais et al.59 and Lantz et al.61). Given that Pi(λ) were calculated using irradiance data measured using a particular spectroradiometer (i.e., Stellarnet EPP2000CSR-100), there is a concern that using the kiendo equation with Ed(0,λ) data measured with a different spectroradiometer model will result in incorrect kiendo predictions. To investigate this we measured the solar spectrum on a cloudless day in Berkeley, CA (Oct 29, 2015; 3 pm), using two spectroradiometer models: Stellarnet EPP2000C-SR-100 and International Light Technologies ILT950. The spectrum was also predicted using SMARTS. The three irradiance spectra were used to predict kiendo of the four bacteria populations (Figure 4). Despite differences among the irradiance spectra (Figure 4a), it is encouraging that kiendo predicted using the three irradiance spectra were not dramatically different from each other. Using the irradiance spectrum predicted by SMARTS, kiendo values were 30−37% greater than those predicted using the irradiance spectrum measured with the Stellarnet radiometer (Figure 4c); using the ILT950 irradiance spectrum resulted in predicted kiendo values that were approximately 10% lower than those predicted with the Stellarnet spectrum. Additionally, calculation of the photodamage spectra (i.e., Pi(λ) × ⟨E0(z,λ)⟩, Figure 4b) highlighted the wavelengths that contributed most to kiendo. Photodamage spectra account for the sensitivity of bacteria to each wavelength of light and the magnitude of light irradiance; the areas under photodamage spectra curves are equal to kiendo. On the basis of this analysis, UVA light was most important for i kendo of E. coli and enterococci, and the peaks of the photodamage spectra were located at λ = 330 nm. If future work finds the discrepancy in irradiance spectra measured by different radiometers to greatly effect predicted kiendo then further research should be conducted to develop a correction factor that can account for variability in measurements by different spectroradiometers. One option would be to use different spectroradiometers to measure the same exact light source (e.g., a quartz−tungsten−halogen lamp or the same solar simulator) and then calculate correction factors for each

Despite underestimating sensitivity coefficients in the UVB region, the first-order exponential Pi(λ) spectra developed from data in the present study can be used to predict sunlight inactivation rates for most field conditions given that UVB light makes up a small percentage of the solar spectrum and is attenuated fastest in the water column. To determine whether our model can estimate indigenous wastewater E. coli and enterococci inactivation rates under natural sunlight, we used irradiance and absorbance data from two previous studies to model total inactivation rates (ktot), which we compared to the reported observed inactivation rates. The two studies were the following: (1) Nguyen et al.,15 who conducted batch reactor inactivation experiments in Discovery Bay, CA (37°N latitude) utilizing water from an open-water wetland fed with nitrified, secondary wastewater effluent, and (2) Maraccini et al.,55 who studied bacteria inactivation in dialysis bags placed at different water depths in Pillar Point Harbor, CA (37°N latitude). Both studies utilized bacteria sourced from wastewater. ktot were modeled as the sum of the endogenous (kendo), exogenous (kexo), and dark (kdark) inactivation rates;15 details are provided in the Supporting Information. kendo were calculated using the model developed in this publication (eq 3); inputs included the water absorbance spectra measured by each study,15,55 the 24 h average sunlight irradiance predicted by the Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS; global horizontal irradiance),56,57 and the absorbance spectrum of dialysis bags used by Maraccini et al.55 kexo and kdark were estimated as described by Nguyen et al.15 There was very good agreement between modeled ktot and the kobs measured by Maraccini et al.55 (Figure 3, Table S3), which we attribute to similar characteristics between Pillar Point Harbor water and the PBS used in experiments

Figure 3. Measured (kobs) and modeled (ktot) inactivation rates of indigenous wastewater bacteria in an open-water wetland (kobs data from Nguyen et al.15) and clear marine water (kobs data from Maraccini et al.55). ktot values were calculated using the approach outlined by Nguyen et al.15 but with the kendo models for wastewater-sourced bacteria developed in this publication. G


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Figure 4. Comparison of natural sunlight irradiance spectra determined by three methods, and their effect on predicted inactivation rates. Irradiance spectra were measured in Berkeley, CA (Oct 29, 2015; 3 pm), and were either predicted using SMARTS, or measured using one of two radiometer models: Stellarnet EPP2000C-SR-100 or International Light Technologies ILT950. (a) Irradiance spectra. (b) Photodamage spectra of laboratorysourced E. coli (i.e., Pi(λ) × ⟨E0 (z,λ)⟩); peaks of the photodamage spectra are located at λ = 330 nm. (c) kendo predicted using each irradiance spectrum.

Nguyen, Peter Maraccini, and Alexandria Boehm for sharing data; Daisy Benitez for her assistance in the laboratory; and the staff at the Town of Discovery Bay for their invaluable assistance at the wetland.

wavelength based on the discrepancy between irradiance measurements. The BWF models presented here can be used to predict endogenous inactivation rates of indigenous E. coli and enterococci in waters that do not have large changes in or extreme values of pH, DO, or temperature. When estimating total sunlight inactivation rates in clear water or for bacteria not susceptible to exogenous inactivation (such as many Gramnegative bacteria,62 including E. coli7,62), the endogenous inactivation rate model can be used on its own. If the water matrix contains photosensitizers and the bacteria are susceptible to the exogenous inactivation mechanism (e.g., enterococci)7 then the endogenous inactivation rate model must be combined with the exogenous rate model presented by Nguyen et al.15 The resulting total, predicted, sunlight inactivation rates can be used by natural wastewater treatment facility designers or managers of recreational waters to determine the hydraulic residence times required to meet disinfection targets.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.6b03721. Additional methodology and supporting figures and tables referenced in the text (PDF) Pi(λ) spectra and 95% confidence interval spectra (XLSX)

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REFERENCES

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: (646) 997-3018. Present Address §

A.I.S.: Department of Civil and Urban Engineering and College of Global Public Health, New York University, Brooklyn, New York 11201, United States. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported by the National Science Foundation (Grant CBET-1335673) and the Engineering Research Center for Reinventing the Nations Urban Water Infrastructure (ReNUWIt; Grant EEC-1028968). We thank Mi H


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