Human-Specific E.coli Single Nucleotide Polymorphism (SNP

Oct 25, 2011 - Recently, we developed a new SNP method for typing E. coli strains, by which human-specific genotypes were identified. Here, we report ...
0 downloads 0 Views 1MB Size
ARTICLE pubs.acs.org/est

Human-Specific E.coli Single Nucleotide Polymorphism (SNP) Genotypes Detected in a South East Queensland Waterway, Australia Maxim S. Sheludchenko, Flavia Huygens,* and Megan H. Hargreaves Cell and Molecular Biosciences, Faculty of Science and Technology, Queensland University of Technology, Brisbane, Queensland, Australia

bS Supporting Information ABSTRACT: The World Health Organization recommends that the majority of water monitoring laboratories in the world test for E. coli daily since thermotolerant coliforms and E. coli are key indicators for risk assessment of recreational waters. Recently, we developed a new SNP method for typing E. coli strains, by which human-specific genotypes were identified. Here, we report the presence of these previously described specific SNP profiles in environmental water, sourced from the Coomera River, located in South East Queensland, Australia, over a period of two years. This study tested for the presence of human-specific E. coli to ascertain whether hydrologic and anthropogenic activity plays a key role in the pollution of the investigated watershed or whether the pollution is from other sources. We found six human-specific SNP profiles and one animalspecific SNP profile consistently across sampling sites and times. We have demonstrated that our SNP genotyping method is able to rapidly identify and characterize human- and animal-specific E. coli isolates in water sources.

’ INTRODUCTION Water is a precious resource in Australia, which is the driest inhabited continent. Approximately every ten years in Australia there are three years with sufficient water supply and three years of drought, then water restrictions and other measures are put in place to ensure the supply.1 Environmentally conscientious residents are interested in effective water management strategies and therefore their water authorities are as well. Microbial source tracking (MST) is one of the tools for the identification of multiple sources of fecal pollution in water sources. Various microorganisms are known to be targets for MST.2 Some pathogenic E. coli have been transmitted via water.3 To our knowledge, there are no host-specific markers for E. coli, even among virulence genes and the recently discovered humanspecific serotype O81,4 that have been consistently detected in environmental water.5 Recently, we developed a new SNP method for typing E. coli strains, by which human-specific genotypes were identified.6 Typing methods used for MST rely on building a reference library from known host groups to identify sources in unknown water samples. The most common examples of these methods are repetitive extragenic palindromic (rep) PCR,7 ribotyping,8 and pulsed-field gel electrophoresis (PFGE).9 They are difficult to develop, interpret, and often are applied only in local geographical areas where the reference library was developed initially.10 Therefore, library-dependent methods are rarely used. Alternatively, there are library-independent methods which are usually based on polymerase chain reaction (PCR) only and do not require large reference libraries. These methods have targeted specific genes of microorganisms which are difficult to r 2011 American Chemical Society

cultivate in normal conditions such as Bacteroides spp. 16S rRNA clone groups,11 F+ RNA coliphage differentiation,2 and enteric viruses including polyomaviruses and adenoviruses.12 The advantage of these molecular markers is that they appear to be host-specific,13 although, some studies have reported humanspecific markers from nonhuman sources. For instance, humanspecific Bacteroides marker was detected in other animals such as dogs14 and fish.15 In this study, using E. coli as the target organism, we chose a molecular typing approach that is a comparative method. This method simply determines whether isolates are the same or different based on their SNP genotype profile. The SNP-typing method developed recently6 and applied here is considered by the authors to be neither library-dependent nor -independent. In fact, the authors would describe this novel genotyping approach as a culture-dependent library-based method and once developed, can be done without culture. Library-based due to the origin of the SNPs identified from housekeeping genes described in the MLST database. We report the presence of these previously described specific SNP profiles in environmental water, sourced from the Coomera River, located in South East Queensland, Australia, over a period of two years. The sampling points were suggested by the Gold Coast City Council as being problematic sites with a history of large numbers of fecal coliforms. One potential source of fecal Received: May 11, 2011 Accepted: October 25, 2011 Revised: October 24, 2011 Published: October 25, 2011 10331

dx.doi.org/10.1021/es201599u | Environ. Sci. Technol. 2011, 45, 10331–10336

Environmental Science & Technology

ARTICLE

Table 1. Locations and Characteristics of Sampling Sites site name (GISa map reference)

code

site characteristics

site classification

1

Coomera Marina ( 27.861672, 153.339089)

cattle/kangaroo grazing, house-boat mooring site

rural

2

Santa Barbara ( 27.855165, 153.350612)

park, BBQ, toilets and fishing, private houses about 100 m away

recreational

3

Sanctuary Cove ( 27.851617, 153.362140)

canal estate, modern houses and apartments, modern infrastructure,

urban/suburban

commercial/light industrial area

a

4

Jabiru Island ( 27.879057, 153.380685)

disused sand mine, no houses, small park with sewered toilets

rural

5

Paradise Point ( 27.886359, 153.396596)

public swimming beach, mouth of river, much water traffic

recreational

6

Coombabah, the Estuary ( 27.896607, 153.366845)

established suburban area, uninhabited island opposite

suburban

Global information system.

Table 2. Weather Patterns on Days of Sampling variable

a

Autumn 2008

Winter 2008

Autumn 2009

Winter 2009

rainfall in mm, 24 h prior sampling rainfall in mm, 72 h prior sampling

0 0.4

1.3 33.9

3.2 15.2

1.4 1.4

tides (m)a

0.183

0.425

0.931

1.368

Personal communication (Daryl Metters, Maritime Safety Queensland, Department of Transport and Main Roads).

pollution may be the accidental sewage discharge from a large number of yachts and houseboats owned by residents who have boat moorings in the many canal estates. According to the Transport Operations (Marine Pollution) Act16 and Regulation,17 sewage discharge in canals and marinas is prohibited. Boat owners, however, may be unaware of the regulations, or noncompliant. As a result, sewage discharge into the Coomera estuary may be a continuing risk. The method described in our previous work6 allowed us to test for the presence of humanspecific E. coli in the current study.

’ EXPERIMENTAL SECTION Study Site. The Pimpana-Coomera watershed is located in South East Queensland, Australia. It is used intensively for agriculture and recreation and an anthropogenic effect can be observed in the watershed. The main water source is the Coomera River, which flows for 90 km from its headwaters in the Lamington National Park to the river mouth in the Pacific Ocean. The upper reaches of the river pass through mainly rural areas, where crop and cattle grazing are the common economic/land use activities. In the 1970s and 1980s, the river was widened 20 km upstream from the mouth as a consequence of sand and gravel extraction operations. The lower reaches of the Coomera River pass through highly developed areas including canal estates such as Santa Barbara, Hope Island, Sanctuary Cove, and the Coomera Mooring Marina. Most of the sewage collection system is gravity fed and follows natural catchment drainage lines until it reaches a centrally located wastewater treatment plant. After treatment, the water is released into the Gold Coast Seaway located south of the Coomera River estuary. Despite the existence of such an effective treatment system, large numbers of coliforms have been observed over a long period of time in the estuary by the Gold Coast City council. Environmental Water Sampling. Four seasonal trips to the Coomera catchment on the Gold Coast were undertaken to collect 24 river water samples (taken in duplicate) from May 2008 to July 2009. Sites selected for sampling included the following: Coomera Marina (1), Santa Barbara (2), Sanctuary Cove (3), Jabiru Island (4), Paradise Point (5), and Coombabah (6) (TOC Art Figure and Table 1). These were suggested by the

Gold Coast City Council as being problematic sites with a history of high concentrations of fecal coliforms. Two water samples of 600 mL each were collected in sterile bottles containing sodium thiosulphate (in case of chlorine residuals in the sample) and transported on ice to the laboratory. Samples were collected using a 1.8 m long dipper, to ensure that the sample was taken from the river rather than the edge where eddies and ephemeral contamination may have interfered with the results. Water samples were prepared for analyses immediately upon arrival at the laboratory, which was always within 6 h. Rainfall and tidal information for sampling days/times were retrieved from the Australian Bureau of Meteorology and are listed in Table 2 (http://reg.bom.gov.au/climate/data/index.shtmLand). Isolation of E. coli. The environmental water samples collected in duplicate were each mixed thoroughly, and 100 mL was filtered as described previously.18 As a high number of E. coli was expected based on previous studies of these sites (pilot study and Gold Coast Council data), samples were filtered both undiluted and diluted 1:10 to provide a countable number of colonies per filter pad. Testing according to the U.S. EPA standard19 was done on all water samples and dilutions, using 0.45-μm sterile gridded filter membranes (Millipore Corporation, Bedford, MA). The membranes were aseptically transferred to modified mTEC agar plates (BD, Sparks, MD) and were incubated aerobically at 35 °C for 2 h followed by an additional incubation at 44.5 °C for 24 h. Single magenta colonies demonstrating ß-D-galactosidase activity were selected and subcultured onto MacConkey agar #2 (Oxoid, UK) for DNA extraction as described previously.20 DNA Extraction. To prepare genomic DNA, single lactose fermenting pink colonies from MacConkey agar #2 were isolated and incubated overnight in 5 mL of nutrient broth (Oxoid, UK). A 500 μL portion of overnight culture was centrifuged at 10 000g for 1 min. Cell pellets were resuspended in 180 μL of DNAase/ RNAase-free water and used for DNA extraction on the Corbett X-tractorGene automated DNA extraction system (Corbett Robotics, Brisbane, Australia, Protocol no.141404 version 02). Quantity and purity of DNA extracts were tested on the DU 730 spectrophotometer (Beckman Coulter, USA). Identification of E. coli SNP Genotypes by Allele-Specific Real-Time PCR. The E. coli MLST database available at NIH 10332

dx.doi.org/10.1021/es201599u |Environ. Sci. Technol. 2011, 45, 10331–10336

Environmental Science & Technology

ARTICLE

Figure 1. Shades of red and black indicate human-specific SNP profiles. Shades of green indicate animal-specific SNP profiles. Blue indicates unique SNP profiles isolated from the Coomera River. The remaining colors are indicative of “mixed source” SNP profiles. Uncolored spaces indicate SNP profiles that have only been detected once or twice. Circles show values of E. coli in Colony Forming Units (CFU)/mL.

(http://www.shigatox.net) currently contains 668 E. coli strains that are grouped into 231 Sequence Types (STs). Informative SNP sets were identified by using the software program called “Minimum SNPs”.21 Eight SNPs, with a Simpson’s index of Diversity (D value) D of 0.96, were determined by the program for the differentiation of E. coli isolates.6 A method for highly discriminatory SNP interrogation of E. coli, by using allelespecific real-time PCR, was developed previously by our group6

and applied to all the E. coli isolates in this study. A number of SNP profiles resolved the E. coli population on the basis of unique eight character “barcodes” within a day. From each subculture/ McConkey plate at least 10 colonies, when possible, were selected for DNA extraction followed by SNP profile identification. Statistical Analysis. Binary logistic regression analysis was used to determine the relationship between human/nonhuman SNP profiles and seasons, rainfalls (24 h and 72 h prior sampling), tide 10333

dx.doi.org/10.1021/es201599u |Environ. Sci. Technol. 2011, 45, 10331–10336

Environmental Science & Technology

ARTICLE

Table 3. Summary of the Twenty Most Prevalent SNP Profiles Distributed Across Sampling Sites and Time-Pointsa

a

* indicates SNP profile numbers as previously published in Sheludchenko et al.6

levels for estimating salinity at sampling times, land use categories (suburban, urban, rural, and recreational) and distance from the river mouth to the sampling site. All calculations were done using MiniTab 16.0 (Minitab Inc.) and p-values were calculated. To calculate the SNP diversity per sampling site, any SNP profile that was only found once or twice at a specific site (clear bars in Figure 1), was excluded from this analysis; instead, SNP profiles that were frequently found at each site were included (colored bars in Figure 1). Supporting Information Table S1 provides a summary of the numbers of SNP profiles found per season per sampling site. The total number of SNP profiles found at each sampling point was used in the binary logistic analysis.

’ RESULTS AND DISCUSSION Although it is well recognized that E. coli numbers in natural waterways fluctuate in response to environmental factors, particularly rainfall events,22 there is limited research reporting the diversity of E. coli genotypes in environmental water in relation to similar hydrological conditions and land use.5,23 In the current study, a new highly discriminatory genotyping method based on SNP interrogation of E. coli6 was applied to detect host-specific E. coli genotypes in the Coomera watershed over a two-year period. In total, 165 isolates were grouped into 67 SNP profiles found at six sites collected between May 2008 and July 2009. A summary of all the SNP profiles observed in this study can be found in Table S2. A very low number of E. coli were detected in samples from site 3, which is therefore not discussed further in this paper.

Despite the fact that there was variation in E. coli numbers isolated from the various sampling sites over the period of two years (Figure 1), the SNP type diversity did not vary significantly between sites, or times of sampling. Nor was a significant relationship detected between SNP profile diversity, and rainfall, seasons, tides, rural/suburban land use, or distance from the river mouth. From a SNP profile perspective, we found the most diverse E. coli population at site 1, which also had the lowest number of E. coli. In addition, host-specific profiles were rarely detected at sites 4, 5, or 6, which had the largest number of E. coli. The 20 most prevalent SNP profiles were detected in a number of samples over the whole period (Figure 1 and Table S3). The remaining 47 SNP profiles were detected only once. Of the 20 most prevalent profiles, three human-specific profiles (29, 11, and 32) and one animal-specific profile (7) corresponded to human- and animal-specific profiles published previously.6 Table 3 is a summary of the 20 most prevalent SNP profiles found at each sampling site and at each sampling time-point. Human- and animal-specific SNP profiles were distributed across all the sampling sites, irrespective of the seasons. Five of the top 20 SNP profiles (35, 70, 37, 80 and 40) could also be considered to be representative of human fecal contamination, since these profiles were previously characterized as “mainly human-specific”.6 Also included in the most prevalent cohort were SNP profiles previously identified as “nonspecific” (9, 21, 22, 23, 26, and 34) in terms of host assignation.6 SNP profile 80, which in our previous study contained only two human isolates (and no animal), was identified in four of the six sampling sites.6 SNP profiles 14 and 82 were also detected, 10334

dx.doi.org/10.1021/es201599u |Environ. Sci. Technol. 2011, 45, 10331–10336

Environmental Science & Technology which have previously been characterized as cattle- and horsesourced, respectively.6 Interestingly, SNP profile 45 was not found at any of the sites we tested, even though this profile was shown by our previous study to be prevalent in local and international collections.6 In addition, we have not found SNP profile 76 in this or our previous study,6 despite the fact that it is a profile found in other countries. In conclusion, we found six human-specific (29, 11, 32, 70, 37, 80) SNP profiles and one animal-specific (7) SNP profile consistently across sampling sites and times. SNP profile 29 was found in the majority (44%) of samples tested in this study. SNP profile 11 was the second-most commonly encountered profile, being present in 28% of samples (Table S2). This study investigated SNP profiles, previously aligned with human or nonhuman sources,6 found in an E. coli population isolated from a natural waterway. The effect of variables such as rainfall (24 and 72 h), tide height and time, general land use (rural, suburban), seasons, and distance from the river mouth as an estimate of salinity was investigated and it was found that none of the variables significantly influenced the diversity of E. coli SNP profiles present in the water (p values >0.35). In addition, by applying our previously developed SNP genotyping method6 to genotype water-sourced E. coli, we were able to identify six human-specific E. coli SNP profiles, and four animal-specific E. coli SNP profiles in the Coomera River of South East Queensland, Australia.

’ ASSOCIATED CONTENT

bS

Supporting Information. Table S1 lists the number of SNP profiles found more frequently at each sampling point per season; Table S2 lists all the SNP profiles found in this study; Table S3 lists the abundance of SNP genotypes in the Coomera water catchment over a period of two years and four sampling events. This information is available free of charge via the Internet at http://pubs.acs.org/

’ AUTHOR INFORMATION Corresponding Author

*Phone: +61 7 3138 0453; fax: +61 7 3138 1534; e-mail: [email protected].

’ ACKNOWLEDGMENT We thank Melanie Robertson-Dean for providing assistance with statistical analyses. M.S.S. is in receipt of a postgraduate studentship from the Institute of Sustainable Resources, Queensland University of Technology. ’ REFERENCES (1) Department of the Environment, W., Heritage and the Arts. Australian weather and the seasons. http://www.culture.gov.au/articles/weather/. Visited April 2011. (2) Bernhard, A.; Field, K. Identification of nonpoint sources of fecal pollution in coastal waters by using host-specific 16S ribosomal DNA genetic markers from fecal anaerobes. Appl. Environ. Microbiol. 2000, 66 (4), 1587–1594. (3) King, E. L.; Bachoon, D. S.; Gates, K. W. Rapid detection of human fecal contamination in estuarine environments by PCR targeting of Bifidobacterium adolescentis. J. Microbiol. Methods 2007, 68 (1), 76–8.

ARTICLE

(4) Scott, T. M.; Jenkins, T. M.; Lukasik, J.; Rose, J. B. Potential use of a host associated molecular marker in Enterococcus faecium as an index of human fecal pollution. Environ. Sci. Technol. 2005, 39 (1), 283–287. (5) Havelaar, A. H.; Furuse, K.; Hogeboom, W. M. Bacteriophages and indicator bacteria in human and animal faeces. J. Appl. Bacteriol. 1986, 60 (3), 255–262. (6) Sheludchenko, M. S.; Huygens, F.; Hargreaves, M. H. Highlydiscriminatory single nucleotide polymorphism interrogation of Escherichia coli using allele-specific Real-Time-PCR and eBURST analysis. Appl. Environ. Microbiol. 2010, 76 (13), 4337–4345. (7) Walk, S. T.; Alm, E. W.; Gordon, D. M.; Ram, J. L.; Toranzos, G. A.; Tiedje, J. M.; Whittam, T. S. Cryptic lineages of the genus Escherichia. Appl. Environ. Microbiol. 2009, 75 (20), 6534–6544. (8) Clermont, O.; Lescat, M.; O’Brien, C. L.; Gordon, D. M.; Tenaillon, O.; Denamur, E. Evidence for a human-specific Escherichia coli clone. Environ. Microbiol. 2008, 10 (4), 1000–1006. (9) Ahmed, W.; Tucker, J.; Bettelheim, K. A.; Neller, R.; Katouli, M. Detection of virulence genes in Escherichia coli of an existing metabolic fingerprint database to predict the sources of pathogenic E. coli in surface waters. Water Res. 2007, 41, 3785–3791. (10) Ratajczak, M.; Laroche, E.; Berthe, T.; Clermont, O.; Pawlak, B.; Denamur, E.; Petit, F. Influence of hydrological conditions on the Escherichia coli population structure in the water of a creek on a rural watershed. BMC Microbiol. 2010, 10 (1), 222. (11) Smith, C. J.; Olszewski, A. M.; Mauro, S. A. Correlation of shiga toxin gene frequency with commonly used microbial indicators of recreational water quality. Appl. Environ. Microbiol. 2009, 75 (2), 316–321. (12) Kon, T.; Weir, S. C.; Howell, E. T.; Lee, H.; Trevors, J. T. Repetitive element (REP)-polymerase chain reaction (PCR) analysis of Escherichia coli isolates from recreational waters of southeastern Lake Huron. Can. J. Microbiol. 2009, 55 (1), 269–276. (13) D’Elia, T. V.; Cooper, C. R.; Johnston, C. G. Source tracking of Escherichia coli by 16S 23S intergenic spacer region denaturing gradient gel electrophoresis (DGGE) of the rrnB ribosomal operon. Can. J. Microbiol. 2007, 53, 1174–1184. (14) McLellan, S. L.; Daniels, A. D.; Salmore, A. K. Genetic characterization of Escherichia coli populations from host sources of fecal pollution by using DNA fingerprinting. Appl. Environ. Microbiol. 2003, 69 (5), 2587–2594. (15) Seurinck, S.; Verstraete, W.; Siciliano, S. Microbial source tracking for identification of fecal pollution. Rev. Environ. Sci. Biotechnol. 2005, 4 (1), 19–37. (16) Yan, T.; Sadowsky, M. J. Determining sources of fecal bacteria in waterways. Environ. Monit. Assess. 2007, 129 (1 3), 97–106. (17) Bernhard, A. E.; Field, K. G. A PCR assay to discriminate human and ruminant feces on the basis of host differences in BacteroidesPrevotella genes encoding 16S rRNA. Appl. Environ. Microbiol. 2000, 66 (10), 4571–4574. (18) Fong, T. T.; Lipp, E. K. Enteric viruses of humans and animals in aquatic environments: Health risks, detection, and potential water quality assessment tools. Microbiol. Mol. Biol. Rev. 2005, 69 (2), 357–361. (19) Ogorzaly, L.; Tissier, A.; Bertrand, I.; Maul, A.; Gantzer, C. Relationship between F-specific RNA phage genogroups, faecal pollution indicators and human adenoviruses in river water. Water Res. 2009, 43 (5), 1257–1264. (20) McQuaig, S. M.; Scott, T. M.; Harwood, V. J.; Farrah, S. R.; Lukasik, J. O. Detection of human-derived fecal pollution in environmental waters by use of a PCR-based human polyomavirus assay. Appl. Environ. Microbiol. 2006, 72 (12), 7567–7574. (21) Ahmed, W.; Goonetilleke, A.; Powell, D.; Gardner, T. Evaluation of multiple sewage-assoclated Bacteroides PCR markers for sewage pollution tracking. Water Res. 2009, 43 (19), 4872–4877. (22) Kildare, B. J.; Leutenegger, C. M.; McSwain, B. S.; Bambic, D. G.; Rajal, V. B.; Wuertz, S. 16S rRNA-based assays for quantitative detection of universal, human-, cow-, and dog-specific fecal Bacteroidales: A Bayesian approach. Water Res. 2007, 41 (16), 3701–3715. 10335

dx.doi.org/10.1021/es201599u |Environ. Sci. Technol. 2011, 45, 10331–10336

Environmental Science & Technology

ARTICLE

(23) McLain, J. E. T.; Ryu, H.; Kabiri-Badr, L.; Rock, C. M.; Abbaszadegan, M. Lack of specificity for PCR assays targeting human Bacteroides 16S rRNA gene: Cross-amplification with fish feces. FEMS Microbiol. Lett. 2009, 299 (1), 38–43. (24) MSQ, Transport Operations (Marine Pollution) Act. Queensland, M. S., Ed. 1995. (25) MSQ, Transport Operations (Marine Pollution) Regulation. Queensland, M. S., Ed. 2008. (26) Eaton, A.; Clesceri, L. S.; Rice, E. W.; Greenberg, A. E. Standard Methods for the Examination of Water and Wastewater, 21st ed.; Washington, DC, 2005. (27) Oshiro, R. Method 1603: Escherichia coli (E.coli) in water by membrane filtration using modified membrane-thermotolerant Escherichia coli agar (Modified mTEC); EPA 600-4-85-076; Office of Water Regulations and Standards, U.S. Environmental Protection Agency: Washington, DC, 2002. (28) Vogel, J. R.; Stoeckel, D. M.; Lamendella, R.; Zelt, R. B.; Domingo, J. W. S.; Walker, S. R.; Oerther, D. B. Identifying fecal sources in a selected catchment reach using multiple source-tracking tools. J. Environ. Qual. 2007, 36 (3), 718–729. (29) Robertson, G.; Huygens, F.; Giffard, G. Identification and interrogation of highly informative single nucleotide polymorphism sets defined by bacterial multilocus sequence typing databases. J. Med. Microbiol. 2004, 53, 35–45. (30) Kelsey, H.; Porter, D., E.; Scott, G.; Neet, M.; White, D. Using Geographic Information Systems and Regression Analysis to Evaluate Relationships between Land Use and Fecal Coliform Bacterial Pollution; Elsevier: Kidlington, Royaume-Uni, 2004; p 13. (31) Kleinheinz, G. T.; McDermott, C. M.; Hughes, S.; Brown, A. Effects of rainfall on E. coli concentrations at Door County, Wisconsin beaches. Int. J. Microbiol. 2009, 2009, 9. (32) Ishii, S.; Hansen, D. L.; Hicks, R. E.; Sadowsky, M. J. Beach sand and sediments are temporal sinks and sources of Escherichia coli in Lake Superior. Environ. Sci. Technol. 2007, 41 (7), 2203–2209. (33) Lyautey, E.; Lu, Z.; Lapen, D. R.; Wilkes, G.; Scott, A.; Berkers, T.; Edge, T. A.; Topp, E. Distribution and diversity of Escherichia coli populations in the South Nation river drainage basin, Eastern Ontario, Canada. Appl. Environ. Microbiol. 2010, 76 (5), 1486–1496. (34) Wijesinghe, R. U.; Feng, Y.; Wood, C. W.; Stoeckel, D. M.; Shaw, J. N. Population dynamics and genetic variability of Escherichia coli in a mixed land-use watershed. J. Water Health 2009, 07 (3), 484–496.

10336

dx.doi.org/10.1021/es201599u |Environ. Sci. Technol. 2011, 45, 10331–10336